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Mining and summarizing customer reviews


Abstract and Figures

Merchants selling products on the Web often ask their customers to review the products that they have purchased and the associated services. As e-commerce is becoming more and more popular, the number of customer reviews that a product receives grows rapidly. For a popular product, the number of reviews can be in hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. It also makes it difficult for the manufacturer of the product to keep track and to manage customer opinions. For the manufacturer, there are additional difficulties because many merchant sites may sell the same product and the manufacturer normally produces many kinds of products. In this research, we aim to mine and to summarize all the customer reviews of a product. This summarization task is different from traditional text summarization because we only mine the features of the product on which the customers have expressed their opinions and whether the opinions are positive or negative. We do not summarize the reviews by selecting a subset or rewrite some of the original sentences from the reviews to capture the main points as in the classic text summarization. Our task is performed in three steps: (1) mining product features that have been commented on by customers; (2) identifying opinion sentences in each review and deciding whether each opinion sentence is positive or negative; (3) summarizing the results. This paper proposes several novel techniques to perform these tasks. Our experimental results using reviews of a number of products sold online demonstrate the effectiveness of the techniques.
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Mining and Summarizing Customer Reviews
Minqing Hu and Bing Liu
Department of Computer Science
University of Illinois at Chicago
851 South Morgan Street
Chicago, IL 60607-7053
{mhu1, liub}
Merchants selling products on the Web often ask their customers
to review the products that they have purchased and the
associated services. As e-commerce is becoming more and more
popular, the number of customer reviews that a product receives
grows rapidly. For a popular product, the number of reviews can
be in hundreds or even thousands. This makes it difficult for a
potential customer to read them to make an informed decision on
whether to purchase the product. It also makes it difficult for the
manufacturer of the product to keep track and to manage customer
opinions. For the manufacturer, there are additional difficulties
because many merchant sites may sell the same product and the
manufacturer normally produces many kinds of products. In this
research, we aim to mine and to summarize all the customer
reviews of a product. This summarization task is different from
traditional text summarization because we only mine the features
of the product on which the customers have expressed their
opinions and whether the opinions are positive or negative. We do
not summarize the reviews by selecting a subset or rewrite some
of the original sentences from the reviews to capture the main
points as in the classic text summarization. Our task is performed
in three steps: (1) mining product features that have been
commented on by customers; (2) identifying opinion sentences in
each review and deciding whether each opinion sentence is
positive or negative; (3) summarizing the results. This paper
proposes several novel techniques to perform these tasks. Our
experimental results using reviews of a number of products sold
online demonstrate the effectiveness of the techniques.
Categories and Subject Descriptors
H.2.8 [Database Management]: Database Applications – data
mining. I.2.7 [Artificial Intelligence]: Natural Language
Processing – text analysis.
General Terms
Algorithms, Experimentation, Human Factors.
Text mining, sentiment classification, summarization, reviews.
With the rapid expansion of e-commerce, more and more products
are sold on the Web, and more and more people are also buying
products online. In order to enhance customer satisfaction and
shopping experience, it has become a common practice for online
merchants to enable their customers to review or to express
opinions on the products that they have purchased. With more and
more common users becoming comfortable with the Web, an
increasing number of people are writing reviews. As a result, the
number of reviews that a product receives grows rapidly. Some
popular products can get hundreds of reviews at some large
merchant sites. Furthermore, many reviews are long and have
only a few sentences containing opinions on the product. This
makes it hard for a potential customer to read them to make an
informed decision on whether to purchase the product. If he/she
only reads a few reviews, he/she may get a biased view. The large
number of reviews also makes it hard for product manufacturers
to keep track of customer opinions of their products. For a product
manufacturer, there are additional difficulties because many
merchant sites may sell its products, and the manufacturer may
(almost always) produce many kinds of products.
In this research, we study the problem of generating feature-based
summaries of customer reviews of products sold online. Here,
features broadly mean product features (or attributes) and
functions. Given a set of customer reviews of a particular product,
the task involves three subtasks: (1) identifying features of the
product that customers have expressed their opinions on (called
product features); (2) for each feature, identifying review
sentences that give positive or negative opinions; and (3)
producing a summary using the discovered information.
Let us use an example to illustrate a feature-based summary.
Assume that we summarize the reviews of a particular digital
camera, digital_camera_1. The summary looks like the following:
Feature: picture quality
Positive: 253
<individual review sentences>
Negative: 6
<individual review sentences>
Feature: size
Positive: 134
<individual review sentences>
Negative: 10
<individual review sentences>
Figure 1: An example summary
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KDD’04, August 22–25, 2004, Seattle, Washington, USA.
Copyright 2004 ACM 1-58113-888-1/04/0008…$5.00.
Research Track Paper
In Figure 1, picture quality and (camera) size are the product
features. There are 253 customer reviews that express positive
opinions about the picture quality, and only 6 that express
negative opinions. The <individual review sentences> link points
to the specific sentences and/or the whole reviews that give
positive or negative comments about the feature.
With such a feature-based summary, a potential customer can
easily see how the existing customers feel about the digital
camera. If he/she is very interested in a particular feature, he/she
can drill down by following the <individual review sentences>
link to see why existing customers like it and/or what they
complain about. For a manufacturer, it is possible to combine
summaries from multiple merchant sites to produce a single report
for each of its products.
Our task is different from traditional text summarization [15, 39,
36] in a number of ways. First of all, a summary in our case is
structured rather than another (but shorter) free text document as
produced by most text summarization systems. Second, we are
only interested in features of the product that customers have
opinions on and also whether the opinions are positive or
negative. We do not summarize the reviews by selecting or
rewriting a subset of the original sentences from the reviews to
capture their main points as in traditional text summarization.
As indicated above, our task is performed in three main steps:
(1) Mining product features that have been commented on by
customers. We make use of both data mining and natural
language processing techniques to perform this task. This
part of the study has been reported in [19]. However, for
completeness, we will summarize its techniques in this paper
and also present a comparative evaluation.
(2) Identifying opinion sentences in each review and deciding
whether each opinion sentence is positive or negative. Note
that these opinion sentences must contain one or more
product features identified above. To decide the opinion
orientation of each sentence (whether the opinion expressed
in the sentence is positive or negative), we perform three
subtasks. First, a set of adjective words (which are normally
used to express opinions) is identified using a natural
language processing method. These words are also called
opinion words in this paper. Second, for each opinion word,
we determine its semantic orientation, e.g., positive or
negative. A bootstrapping technique is proposed to perform
this task using WordNet [29, 12]. Finally, we decide the
opinion orientation of each sentence. An effective algorithm
is also given for this purpose.
(3) Summarizing the results. This step aggregates the results of
previous steps and presents them in the format of Figure 1.
Section 3 presents the detailed techniques for performing these
tasks. A system, called FBS (Feature-Based Summarization), has
also been implemented. Our experimental results with a large
number of customer reviews of 5 products sold online show that
FBS and its techniques are highly effectiveness.
Our work is closely related to Dave, Lawrence and Pennock’s
work in [9] on semantic classification of reviews. Using available
training corpus from some Web sites, where each review already
has a class (e.g., thumbs-up and thumbs-downs, or some other
quantitative or binary ratings), they designed and experimented a
number of methods for building sentiment classifiers. They show
that such classifiers perform quite well with test reviews. They
also used their classifiers to classify sentences obtained from Web
search results, which are obtained by a search engine using a
product name as the search query. However, the performance was
limited because a sentence contains much less information than a
review. Our work differs from theirs in three main aspects: (1)
Our focus is not on classifying each review as a whole but on
classifying each sentence in a review. Within a review some
sentences may express positive opinions about certain product
features while some other sentences may express negative
opinions about some other product features. (2) The work in [9]
does not mine product features from reviews on which the
reviewers have expressed their opinions. (3) Our method does not
need a corpus to perform the task.
In [30], Morinaga et al. compare reviews of different products in
one category to find the reputation of the target product.
However, it does not summarize reviews, and it does not mine
product features on which the reviewers have expressed their
opinions. Although they do find some frequent phrases indicating
reputations, these phrases may not be product features (e.g.,
“doesn’t work”, “benchmark result” and “no problem(s)”). In [5],
Cardie et al discuss opinion-oriented information extraction. They
aim to create summary representations of opinions to perform
question answering. They propose to use opinion-oriented
“scenario templates” to act as summary representations of the
opinions expressed in a document, or a set of documents. Our task
is different. We aim to identify product features and user opinions
on these features to automatically produce a summary. Also, no
template is used in our summary generation.
Our work is also related to but different from subjective genre
classification, sentiment classification, text summarization and
terminology finding. We discuss each of them below.
2.1 Subjective Genre Classification
Genre classification classifies texts into different styles, e.g.,
“editorial”, “novel”, “news”, “poem” etc. Although some
techniques for genre classification can recognize documents that
express opinions [23, 24, 14], they do not tell whether the
opinions are positive or negative. In our work, we need to
determine whether an opinion is positive or negative and to
perform opinion classification at the sentence level rather than at
the document level.
A more closely related work is [17], in which the authors
investigate sentence subjectivity classification and concludes that
the presence and type of adjectives in a sentence is indicative of
whether the sentence is subjective or objective. However, their
work does not address our specific task of determining the
semantic orientations of those subjective sentences. Neither do
they find features on which opinions have been expressed.
2.2 Sentiment Classification
Works of Hearst [18] and Sack [35] on sentiment-based
classification of entire documents use models inspired by
cognitive linguistics. Das and Chen [8] use a manually crafted
lexicon in conjunction with several scoring methods to classify
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stock postings on an investor bulletin. Huettner and Subasic [20]
also manually construct a discriminant-word lexicon and use
fuzzy logic to classify sentiments. Tong [41] generates sentiment
timelines. It tracks online discussions about movies and displays a
plot of the number of positive and negative sentiment messages
over time. Messages are classified by looking for specific phrases
that indicate the author’s sentiment towards the movie (e.g.,
“great acting”, “wonderful visuals”, “uneven editing”). Each
phrase must be manually added to a special lexicon and manually
tagged as indicating positive or negative sentiment. The lexicon is
domain dependent (e.g., movies) and must be rebuilt for each new
domain. In contrast, in our work, we only manually create a small
list of seed adjectives tagged with positive or negative labels. Our
seed adjective list is also domain independent. An effective
technique is proposed to grow this list using WordNet.
Turney’s work in [42] applies a specific unsupervised learning
technique based on the mutual information between document
phrases and the words “excellent” and “poor”, where the mutual
information is computed using statistics gathered by a search
engine. Pang et al. [33] examine several supervised machine
learning methods for sentiment classification of movie reviews
and conclude that machine learning techniques outperform the
method that is based on human-tagged features although none of
existing methods could handle the sentiment classification with a
reasonable accuracy. Our work differs from these works on
sentiment classification in that we perform classification at the
sentence level while they determine the sentiment of each
document. They also do not find features on which opinions have
been expressed, which is very important in practice.
2.3 Text Summarization
Existing text summarization techniques mainly fall in one of the
two categories: template instantiation and passage extraction.
Work in the former framework includes [10, 39]. They emphasize
on identification and extraction of certain core entities and facts in
a document, which are packaged in a template. This framework
requires background knowledge in order to instantiate a template
to a suitable level of detail. Therefore, it is not domain or genre
independent [37, 38]. This is different from our work as our
techniques do not fill any template and are domain independent.
The passage extraction framework [e.g., 32, 25, 36] identifies
certain segments of the text (typically sentences) that are the most
representative of the document’s content. Our work is different in
that we do not extract representative sentences, but identify and
extract those specific product features and the opinions related to
Boguraev and Kennedy [2] propose to find a few very prominent
expressions, objects or events in a document and use them to help
summarize the document. Our work is again different as we find
all product features in a set of customer reviews regardless
whether they are prominent or not. Thus, our summary is not a
traditional text summary.
Most existing works on text summarization focus on a single
document. Some researchers also studied summarization of
multiple documents covering similar information. Their main
purpose is to summarize the similarities and differences in the
information content among these documents [27]. Our work is
related but quite different because we aim to find the key features
that are talked about in multiple reviews. We do not summarize
similarities and differences of reviews.
2.4 Terminology Finding
In terminology finding, there are basically two techniques for
discovering terms in corpora: symbolic approaches that rely on
syntactic description of terms, namely noun phrases, and
statistical approaches that exploit the fact that the words
composing a term tend to be found close to each other and
reoccurring [21, 22, 7, 6]. However, using noun phrases tends to
produce too many non-terms (low precision), while using
reoccurring phrases misses many low frequency terms, terms with
variations, and terms with only one word. Our association mining
based technique does not have these problems, and we can also
find infrequent features by exploiting the fact that we are only
interested in features that the users have expressed opinions on.
Figure 2 gives the architectural overview of our opinion
summarization system.
The inputs to the system are a product name and an entry Web
page for all the reviews of the product. The output is the summary
of the reviews as the one shown in the introduction section.
The system performs the summarization in three main steps (as
discussed before): (1) mining product features that have been
commented on by customers; (2) identifying opinion sentences in
each review and deciding whether each opinion sentence is
positive or negative; (3) summarizing the results. These steps are
performed in multiple sub-steps.
Given the inputs, the system first downloads (or crawls) all the
reviews, and put them in the review database. It then finds those
“hot” (or frequent) features that many people have expressed their
opinions on. After that, the opinion words are extracted using the
Opinion Sentence Orientation Identification
Summary Generation
Figure 2: Feature-based opinion summarization
Crawl Reviews
POS Tagging
Feature Pruning
Frequent Feature
Opinion word
Opinion Orientation
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resulting frequent features, and semantic orientations of the
opinion words are identified with the help of WordNet. Using the
extracted opinion words, the system then finds those infrequent
features. In the last two steps, the orientation of each opinion
sentence is identified and a final summary is produced. Note that
POS tagging is the part-of-speech tagging [28] from natural
language processing, which helps us to find opinion features.
Below, we discuss each of the sub-steps in turn.
3.1 Part-of-Speech Tagging (POS)
Product features are usually nouns or noun phrases in review
sentences. Thus the part-of-speech tagging is crucial. We used the
NLProcessor linguistic parser [31] to parse each review to split
text into sentences and to produce the part-of-speech tag for each
word (whether the word is a noun, verb, adjective, etc). The
process also identifies simple noun and verb groups (syntactic
chunking). The following shows a sentence with POS tags.
<S> <NG><W C='PRP' L='SS' T='w' S='Y'> I </W> </NG>
<VG> <W C='VBP'> am </W><W C='RB'> absolutely
</W></VG> <W C='IN'> in </W> <NG> <W C='NN'> awe
</W> </NG> <W C='IN'> of </W> <NG> <W C='DT'> this
</W> <W C='NN'> camera </W></NG><W C='.'> .
NLProcessor generates XML output. For instance, <W C=‘NN’>
indicates a noun and <NG> indicates a noun group/noun phrase.
Each sentence is saved in the review database along with the POS
tag information of each word in the sentence. A transaction file is
then created for the generation of frequent features in the next
step. In this file, each line contains words from one sentence,
which includes only the identified nouns and noun phrases of the
sentence. Other components of the sentence are unlikely to be
product features. Some pre-processing of words is also performed,
which includes removal of stopwords, stemming and fuzzy
matching. Fuzzy matching is used to deal with word variants and
misspellings [19].
3.2 Frequent Features Identification
This sub-step identifies product features on which many people
have expressed their opinions. Before discussing frequent feature
identification, we first give some example sentences from some
reviews to describe what kinds of opinions that we will be
handling. Since our system aims to find what people like and
dislike about a given product, how to find the product features
that people talk about is the crucial step. However, due to the
difficulty of natural language understanding, some types of
sentences are hard to deal with. Let us see an easy and a hard
sentence from the reviews of a digital camera:
“The pictures are very clear.”
In this sentence, the user is satisfied with the picture quality of the
camera, picture is the feature that the user talks about. While the
feature of this sentence is explicitly mentioned in the sentence,
some features are implicit and hard to find. For example,
“While light, it will not easily fit in pockets.”
This customer is talking about the size of the camera, but the word
size does not appear in the sentence. In this work, we focus on
finding features that appear explicitly as nouns or noun phrases in
the reviews. We leave finding implicit features to our future work.
Here, we focus on finding frequent features, i.e., those features
that are talked about by many customers (finding infrequent
features will be discussed later). For this purpose, we use
association mining [1] to find all frequent itemsets. In our context,
an itemset is simply a set of words or a phrase that occurs together
in some sentences.
The main reason for using association mining is because of the
following observation. It is common that a customer review
contains many things that are not directly related to product
features. Different customers usually have different stories.
However, when they comment on product features, the words that
they use converge. Thus using association mining to find frequent
itemsets is appropriate because those frequent itemsets are likely
to be product features. Those noun/noun phrases that are
infrequent are likely to be non-product features.
We run the association miner CBA [26], which is based on the
Apriori algorithm in [1] on the transaction set of noun/noun
phrases produced in the previous step. Each resulting frequent
itemset is a possible feature. In our work, we define an itemset as
frequent if it appears in more than 1% (minimum support) of the
review sentences. The generated frequent itemsets are also called
candidate frequent features in this paper.
However, not all candidate frequent features generated by
association mining are genuine features. Two types of pruning are
used to remove those unlikely features.
Compactness pruning: This method checks features that contain
at least two words, which we call feature phrases, and remove
those that are likely to be meaningless.
The association mining algorithm does not consider the position
of an item (or word) in a sentence. However, in a sentence, words
that appear together in a specific order are more likely to be
meaningful phrases. Therefore, some of the frequent feature
phrases generated by association mining may not be genuine
features. Compactness pruning aims to prune those candidate
features whose words do not appear together in a specific order.
See [19] for the detailed definition of compactness and also the
pruning procedure.
Redundancy pruning: In this step, we focus on removing
redundant features that contain single words. To describe the
meaning of redundant features, we use the concept of p-support
(pure support). p-support of feature ftr is the number of sentences
that ftr appears in as a noun or noun phrase, and these sentences
must contain no feature phrase that is a superset of ftr.
We use a minimum p-support value to prune those redundant
features. If a feature has a p-support lower than the minimum p-
support (in our system, we set it to 3) and the feature is a subset of
another feature phrase (which suggests that the feature alone may
not be interesting), it is pruned. For instance, life by itself is not a
useful feature while battery life is a meaningful feature phrase.
See [19] for more explanations.
3.3 Opinion Words Extraction
We now identify opinion words. These are words that are
primarily used to express subjective opinions. Clearly, this is
related to existing work on distinguishing sentences used to
express subjective opinions from sentences used to objectively
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describe some factual information [43]. Previous work on
subjectivity [44, 4] has established a positive statistically
significant correlation with the presence of adjectives. Thus the
presence of adjectives is useful for predicting whether a sentence
is subjective, i.e., expressing an opinion. This paper uses
adjectives as opinion words. We also limit the opinion words
extraction to those sentences that contain one or more product
features, as we are only interested in customers’ opinions on these
product features. Let us first define an opinion sentence.
Definition: opinion sentence
If a sentence contains one or more product features and one or
more opinion words, then the sentence is called an opinion
We extract opinion words in the following manner (Figure 3):
for each sentence in the review database
if (it contains a frequent feature, extract all the adjective
words as opinion words)
for each feature in the sentence
the nearby adjective is recorded as its effective
opinion. /* A nearby adjective refers to the adjacent
adjective that modifies the noun/noun phrase that is a
frequent feature. */
Figure 3: Opinion word extraction
For example, horrible is the effective opinion of strap in The
strap is horrible and gets in the way of parts of the camera you
need access to.” Effective opinions will be useful when we
predict the orientation of opinion sentences.
3.4 Orientation Identification for Opinion
For each opinion word, we need to identify its semantic
orientation, which will be used to predict the semantic orientation
of each opinion sentence. The semantic orientation of a word
indicates the direction that the word deviates from the norm for its
semantic group. Words that encode a desirable state (e.g.,
beautiful, awesome) have a positive orientation, while words that
represent undesirable states have a negative orientation (e.g.,
disappointing). While orientations apply to many adjectives, there
are also those adjectives that have no orientation (e.g., external,
digital) [17]. In this work, we are interested in only positive and
negative orientations.
Unfortunately, dictionaries and similar sources, i.e., WordNet
[29] do not include semantic orientation information for each
word. Hatzivassiloglou and McKeown [16] use a supervised
learning algorithm to infer the semantic orientation of adjectives
from constraints on conjunctions. Although their method achieves
high precision, it relies on a large corpus, and needs a large
amount of manually tagged training data. In Turney’s work [42],
the semantic orientation of a phrase is calculated as the mutual
information between the given phrase and the word “excellent”
minus the mutual information between the given phrase and the
word “poor”. The mutual information is estimated by issuing
queries to a search engine and noting the number of hits. The
paper [42], however, does not report the results of semantic
orientations of individual words/phrases. Instead it only gives the
classification results of reviews. We do not use these techniques
in this paper as both works rely on statistical information from a
rather big corpus. Their methods are also inefficient. For example,
in [42], for each word or phrase, a Web search and a substantial
processing of the returned results are needed.
In this research, we propose a simple and yet effective method by
utilizing the adjective synonym set and antonym set in WordNet
[29] to predict the semantic orientations of adjectives.
In WordNet, adjectives are organized into bipolar clusters, as
illustrated in Figure 4. The cluster for fast/slow, consists of two
half clusters, one for senses of fast and one for senses of slow.
Each half cluster is headed by a head synset, in this case fast and
its antonym slow. Following the head synset is the satellite
synsets, which represent senses that are similar to the sense of the
head adjective. The other half cluster is headed by the reverse
antonymous pair slow/fast, followed by satellite synsets for senses
of slow [12].
In general, adjectives share the same orientation as their
synonyms and opposite orientations as their antonyms. We use
this idea to predict the orientation of an adjective. To do this, the
synset of the given adjective and the antonym set are searched. If
a synonym/antonym has known orientation, then the orientation
of the given adjective could be set correspondingly. As the synset
of an adjective always contains a sense that links to head synset,
the search range is rather large. Given enough seed adjectives
with known orientations, we can almost predict the orientations of
all the adjective words in the review collection.
Thus, our strategy is to use a set of seed adjectives, which we
know their orientations and then grow this set by searching in the
WordNet. To have a reasonably broad range of adjectives, we
first manually come up a set of very common adjectives (in our
experiment, we used 30) as the seed list, e.g. positive adjectives:
great, fantastic, nice, cool and negative adjectives: bad, dull.
Then we resort to WordNet to predict the orientations of all the
adjectives in the opinion word list. Once an adjective’s orientation
is predicted, it is added to the seed list. Therefore, the list grows
in the process.
The complete procedure for predicting semantic orientations for
all the adjectives in the opinion list is shown in Figure 5.
Procedure OrientationPrediction takes the adjective seed list and
a set of opinion words whose orientations need to be determined.
Figure 4: Bipolar adjective structure,
( = similarity; = antonymy)
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It calls procedure OrientationSearch iteratively until no new
opinion word is added to the seed list. Every time an adjective
with its orientation is added to the seed list, the seed list is
updated; therefore calling OrientationSearch repeatedly is
necessary in order to exploit the newly added information.
1. Procedure OrientationPrediction(adjective_list, seed_list)
2. begin
3. do {
4. size1 = # of words in seed_list;
5. OrientationSearch(adjective_list, seed_list);
6. size2 = # of words in seed_list;
7. } while (size1 size2);
8. end
1. Procedure OrientationSearch(adjective_list, seed_list)
2. begin
3. for each adjective wi in adjective_list
4. begin
5. if (wi has synonym s in seed_list)
6. { wi’s orientation= s’s orientation;
7. add wi with orientation to seed_list; }
8. else if (wi has antonym a in seed_list)
9. { wi’s orientation = opposite orientation of a’s
10. add wi with orientation to seed_list; }
11. endfor;
12. end
Figure 5: Predicting the semantic orientations of opinion
Procedure OrientationSearch searches WordNet and the seed list
for each target adjective word to predict its orientation (line 3 to
line 11). In line 5, it searches synset of the target adjective in
WordNet and checks if any synonym has known orientation. If so,
the target orientation is set to the same orientation as the synonym
(line 6) and the target adjective along with the orientation is
inserted into the seed list (line 7). Otherwise, the function
continues to search antonym set of the target word in WordNet
and checks if any antonym has known orientation (line 8). If so,
the target orientation is set to the opposite of the antonym (line 9)
and the target adjective with its orientation is inserted into the
seed list (line 10). If neither synonyms nor antonyms of the target
word have known orientation, the function just continues the same
process for the next adjective since the word’s orientation may be
found in a later call of the procedure with an updated seed list.
For those adjectives that WordNet cannot recognize, they are
discarded as they may not be valid words. For those that we
cannot find orientations, they will also be removed from the
opinion words list and the user will be notified for attention. If the
user feels that the word is an opinion word and knows its
sentiment, he/she can update the seed list. In our experiments,
there is no user involvement (those removed opinion words are
dropped). For the case that the synonyms/antonyms of an
adjective have different known semantic orientations, we use the
first found orientation as the orientation for the given adjective.
3.5 Infrequent Feature Identification
Frequent features are the “hot” features that people comment most
about the given product. However, there are some features that
only a small number of people talked about. These features can
also be interesting to some potential customers and the
manufacturer of the product. The question is how to extract these
infrequent features (association mining is unable to identify such
features)? Considering the following sentences:
“The pictures are absolutely amazing.”
“The software that comes with it is amazing.”
Sentences 1 and 2 share the same opinion word amazing yet
describing different features: sentence 1 is about the pictures, and
sentence 2 is about the software. Since one adjective word can be
used to describe different objects, we could use the opinion words
to look for features that cannot be found in the frequent feature
generation step using association mining.
We extract infrequent features using the procedure in Figure 6:
for each sentence in the review database
if (it contains no frequent feature but one or more opinion
{ find the nearest noun/noun phrase around the opinion
word. The noun/noun phrase is stored in the feature
set as an infrequent feature. }
Figure 6: Infrequent feature extraction
We use the nearest noun/noun phrase as the noun/noun phrase
that the opinion word modifies because that is what happens most
of the time. This simple heuristic seems to work well in practice.
A problem with the infrequent feature identification using opinion
words is that it could also find nouns/noun phrases that are
irrelevant to the given product. The reason for this is that people
can use common adjectives to describe a lot of objects, including
both interesting features that we want and irrelevant ones. This,
however, is not a serious problem because the number of
infrequent features, compared with the number of frequent
features, is small. They account for around 15-20% of the total
number of features as obtained in our experimental results.
Infrequent features are generated for completeness. Moreover,
frequent features are more important than infrequent ones. Since
we rank features according to their p-supports, those wrong
infrequent features will be ranked very low and thus will not
affect most of the users.
3.6 Predicting the Orientations of Opinion
We now reach the step of predicting the orientation of an opinion
sentence, i.e., positive or negative. In general, we use the
dominant orientation of the opinion words in the sentence to
determine the orientation of the sentence. That is, if
positive/negative opinion prevails, the opinion sentence is
regarded as a positive/negative one. In the case where there is the
same number of positive and negative opinion words in the
sentence, we predict the orientation using the average orientation
of effective opinions or the orientation of the previous opinion
sentence (recall that effective opinion is the closest opinion word
for a feature in an opinion sentence). This is an effective method
as our experimental results show. The detailed procedure is
described in Figure 7.
Procedure SentenceOrietation deals with three situations in
Research Track Paper
predicting the semantic orientation of an opinion sentence:
1. The user likes or dislikes most or all the features in one
sentence. The opinion words are mostly either positive or
negative, e.g., there are two positive opinion words, good and
exceptional in “overall this is a good camera with a really
good picture clarity & an exceptional close-up shooting
2. The user likes or dislikes most of the features in one sentence,
but there is an equal number of positive and negative opinion
words, e.g., “the auto and manual along with movie modes
are very easy to use, but the software is not intuitive.” There
is one positive opinion easy and one negative opinion not
intuitive, although the user likes two features and dislikes one.
3. All the other cases.
For case 1, the dominant orientation can be easily identified (line
5 to 10 in the first procedure, SentenceOrietation). This is the
most common case when people express their opinions. For case
2, we use the average orientation of effective opinions of features
instead (line 12 to 18). Effective opinion is assumed to be the
most related opinion for a feature. For case 3, we set the
orientation of the opinion sentence to be the same as the
orientation of previous opinion sentence (line 19). We use the
context information to predict the sentence orientation because in
most cases, people express their positive/negative opinions
together in one text segment, i.e., a few consecutive sentences.
For a sentence that contains a but clause (sub-sentence that starts
with but, however, etc.), which indicates sentimental change for
the features in the clause, we first use the effective opinion in the
clause to decide the orientation of the features. If no opinion
appears in the clause, the opposite orientation of the sentence will
be used.
Note that in the procedure wordOrientation, we do not simply
take the semantic orientation of the opinion word from the set of
opinion words as its orientation in the specific sentence. We also
consider whether there is a negation word such as “no”, “not”,
“yet”, appearing closely around the opinion word. If so, the
opinion orientation of the sentence is the opposite of its original
orientation (lines 4 and 5). By closely we mean that the word
distance between a negation word and the opinion word should
not exceed a threshold (in our experiment, we set it to 5). This
simple method deals with the sentences like “the camera is not
easy to use”, and “it would be nicer not to see little zoom sign on
the side”. This method is quite effective in most cases.
3.7 Summary Generation
After all the previous steps, we are ready to generate the final
feature-based review summary, which is straightforward and
consists of the following steps:
For each discovered feature, related opinion sentences are put
into positive and negative categories according to the opinion
sentences’ orientations. A count is computed to show how
many reviews give positive/negative opinions to the feature.
All features are ranked according to the frequency of their
appearances in the reviews. Feature phrases appear before
single word features as phrases normally are more interesting
to users. Other types of rankings are also possible. For
example, we can also rank features according the number of
reviews that express positive or negative opinions.
The following shows an example summary for the feature
picture” of a digital camera. Note that the individual opinion
sentences (and their corresponding reviews, which are not shown
here) can be hidden using a hyperlink in order to enable the user
to see a global view of the summary easily.
Feature: picture
Positive: 12
Overall this is a good camera with a really good
picture clarity.
The pictures are absolutely amazing - the camera
captures the minutest of details.
After nearly 800 pictures I have found that this camera
takes incredible pictures.
Negative: 2
The pictures come out hazy if your hands shake even
for a moment during the entire process of taking a
Focusing on a display rack about 20 feet away in a
brightly lit room during day time, pictures produced by
this camera were blurry and in a shade of orange.
1. Procedure SentenceOrietation()
2. begin
3. for each opinion sentence si
4. begin
5. orientation = 0;
6. for each opinion word op in si
7. orientation += wordOrientation(op, si);
8. /*Positive = 1, Negative = -1, Neutral = 0*/
9. if (orientation > 0) si’s orientation = Positive;
10. else if (orientation < 0) si’s orientation = Negative;
11. else {
12. for each feature f in si
13. orientation +=
14. wordOrientation(f’s effective opinion, si);
15. if (orientation > 0)
16. si’s orientation = Positive;
17. else if (orientation < 0)
18. si’s orientation = Negative;
19. else si’s orientation = si-1’s orientation;
20. }
21. endfor;
22. end
1. Procedure wordOrientation(word, sentence)
2. begin
3. orientation = orientation of word in seed_list;
4. If (there is NEGATION_WORD appears closely
around word in sentence)
5. orientation = Opposite(orientation);
6. end
Figure 7: Predicting the orientations of opinion sentences
Research Track Paper
A system, called FBS (Feature-Based Summarization), based on
the proposed techniques has been implemented in C++. We now
evaluate FBS from three perspectives:
1. The effectiveness of feature extraction.
2. The effectiveness of opinion sentence extraction.
3. The accuracy of orientation prediction of opinion sentences.
We conducted our experiments using the customer reviews of five
electronics products: 2 digital cameras, 1 DVD player, 1 mp3
player, and 1 cellular phone. The reviews were collected from and C| Products in these sites have a large
number of reviews. Each of the reviews includes a text review
and a title. Additional information available but not used in this
project includes date, time, author name and location (for Amazon
reviews), and ratings.
For each product, we first crawled and downloaded the first 100
reviews. These review documents were then cleaned to remove
HTML tags. After that, NLProcessor [31] is used to generate part-
of-speech tags. Our system is then applied to perform
For evaluation, we manually read all the reviews. For each
sentence in a review, if it shows user’s opinions, all the features
on which the reviewer has expressed his/her opinion are tagged.
Whether the opinion is positive or negative (i.e., the orientation)
is also identified. If the user gives no opinion in a sentence, the
sentence is not tagged as we are only interested in sentences with
opinions in this work. For each product, we produced a manual
feature list. Column “No. of manual features” in Table 1 shows
the number of manual features for each product. All the results
generated by our system are compared with the manually tagged
results. Tagging is fairly straightforward for both product features
and opinions. A minor complication regarding feature tagging is
that features can be explicit or implicit in a sentence. Most
features appear explicitly in opinion sentences, e.g., pictures in
“the pictures are absolutely amazing”. Some features may not
appear in sentences. We call such features implicit features, e.g.,
size in “it fits in a pocket nicely”. Both explicit and implicit
features are easy to identify by the human tagger.
Another issue is that judging opinions in reviews can be
somewhat subjective. It is usually easy to judge whether an
opinion is positive or negative if a sentence clearly expresses an
opinion. However, deciding whether a sentence offers an opinion
or not can be debatable. For those difficult cases, a consensus was
reached between the primary human tagger (the first author of the
paper) and the secondary tagger (the second author of the paper).
Table 1 gives the precision and recall results of the feature
generation function of FBS. We evaluated the results at each step
of our algorithm. In the table, column 1 lists each product.
Columns 3 and 4 give the recall and precision of frequent feature
generation for each product, which uses association mining. The
results indicate that the frequent features contain a lot of errors.
Using this step alone gives poor results, i.e., low precision.
Columns 5 and 6 show the corresponding results after
compactness pruning is performed. We can see that the precision
is improved significantly by this pruning. The recall stays steady.
Columns 7 and 8 give the results after pruning using p-support.
There is another dramatic improvement in the precision. The
recall level almost does not change. The results from Columns 4-8
clearly demonstrate the effectiveness of these two pruning
techniques. Columns 9 and 10 give the results after infrequent
feature identification is done. The recall is improved dramatically.
The precision drops a few percents on average. However, this is
not a major problem because the infrequent features are ranked
rather low, and thus will not affect most users.
To further illustrate the effectiveness of our feature extraction
Table 1: Recall and precision at each step of feature generation
Frequent features
(association mining)
Infrequent feature
Product name
No. of
features Recall Precision Recall Precision Recall Precision Recall Precision
Digital camera1 79 0.671 0.552 0.658 0.634 0.658 0.825 0.822 0.747
Digital camera2 96 0.594 0.594 0.594 0.679 0.594 0.781 0.792 0.710
Cellular phone 67 0.731 0.563 0.716 0.676 0.716 0.828 0.761 0.718
Mp3 player 57 0.652 0.573 0.652 0.683 0.652 0.754 0.818 0.692
DVD player 49 0.754 0.531 0.754 0.634 0.754 0.765 0.797 0.743
Average 69 0.68 0.56 0.67 0.66 0.67 0.79 0.80 0.72
Table 2: Recall and precision of FASTR
Recall Precision No. terms
Digital camera1 0.1898 0.0313 479
Digital camera2 0.1875 0.0442 407
Cellular phone 0.1493 0.0275 364
Mp3 player 0.1403 0.0214 374
DVD player 0.1633 0.0305 262
Average 0.1660 0.0309 377.2
Research Track Paper
step, we compared the features generated using our method with
terms found by the well known and publicly available term
extraction and indexing system, FASTR [11] of Christian
Jacquemin. Table 2 shows the recall and precision of FASTR.
We observe that both the average recall and precision of FASTR
are significantly lower than those of our method. After a close
inspection of the terms generated by FASTR, we see that there are
two major reasons that lead to its poor results. First of all, FASTR
generates a large number of terms, as shown in the fourth column
“No. terms” of Table 2. The average number of terms found by
FASTR is 377. Most of these terms are not product features at all
(although many of them may be noun phrases). Secondly, FASTR
does not find one-word terms, but only term phrases that consist
of two or more words. Our feature extraction method finds both
one-word terms and term phrases. Comparing the results in Table
1 and Table 2, we can clearly see that the proposed method is
much more effective for our task.
Table 3 shows the evaluation results of the other two procedures:
opinion sentence extraction and sentence orientation prediction.
The average recall of opinion sentence extraction is nearly 70%.
The average precision of opinion sentence extraction is 64%.
Note that as indicated earlier determining whether a sentence
expresses an opinion is subjective. Our result analysis indicates
that people like to describe their “stories” with the product lively:
they often mention the situation that they used the product, the
detailed product features used, and also the results they got. While
human taggers do not regard these sentences as opinion sentences
as there is no indication of whether the user likes the features or
not, our system labels these sentences as opinion sentences
because they contain both product features and some opinion
adjectives. This decreases precision. Although these sentences
may not show strong user opinions towards the product features,
they may still be beneficial and useful.
Our system has a good accuracy in predicting sentence
orientations: the average accuracy for the five products is 84%.
This shows that our method of using WordNet to predict adjective
semantic orientations and orientations of opinion sentences are
highly effective.
Table 3: Results of opinion sentence extraction and sentence
orientation prediction
Opinion sentence extraction
Product name Recall Precision
Digital camera1 0.719 0.643 0.927
Digital camera2 0.634 0.554 0.946
Cellular phone 0.675 0.815 0.764
Mp3 player 0.784 0.589 0.842
DVD player 0.653 0.607 0.730
Average 0.693 0.642 0.842
In summary, we can see that our techniques are very promising,
especially for sentence orientation prediction. We believe that
they may be used in practical settings. We also note three main
limitations of our system: (1) We have not dealt with opinion
sentences that need pronoun resolution [40]. For instance, “it is
quiet but powerful”. To understand what it represents, pronoun
resolution needs to be performed. Pronoun resolution is a complex
and computational expensive problem in natural language
processing (NLP). We plan to adapt some existing techniques
from NLP to suit our needs. (2) We only used adjectives as
indicators of opinion orientations of sentences. However, verbs
and nouns can also be used for the purpose, e.g., “I like the feeling
of the camera”, “I highly recommend the camera”. We plan to
address this issue in the future. (3) It is also important to study the
strength of opinions. Some opinions are very strong and some are
quite mild. Highlighting strong opinions (strongly like or dislike)
can be very useful for both individual shoppers and product
In this paper, we proposed a set of techniques for mining and
summarizing product reviews based on data mining and natural
language processing methods. The objective is to provide a
feature-based summary of a large number of customer reviews of
a product sold online. Our experimental results indicate that the
proposed techniques are very promising in performing their tasks.
We believe that this problem will become increasingly important
as more people are buying and expressing their opinions on the
Web. Summarizing the reviews is not only useful to common
shoppers, but also crucial to product manufacturers.
In our future work, we plan to further improve and refine our
techniques, and to deal with the outstanding problems identified
above, i.e., pronoun resolution, determining the strength of
opinions, and investigating opinions expressed with adverbs,
verbs and nouns. Finally, we will also look into monitoring of
customer reviews. We believe that monitoring will be particularly
useful to product manufacturers because they want to know any
new positive or negative comments on their products whenever
they are available. The keyword here is new. Although a new
review may be added, it may not contain any new information.
The research in this paper was supported by the National Science
Foundation under the grant NSF IIS-0307239.
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Research Track Paper
...  Step-2 -Pre-processing is done to remove Stop-words, Hash-tags, @user name, Re-Tweet, Hyper-link, Punctuation symbol and expand smiley's, Acronym, use of NER Tagger [21].  Step-3 -Sentiment classification is done by using SentiWordNet [17] into positive, negative or neutral sentiments.  Step 4 -If neutral sentiments occur then re-classification takes place by breaking into tokens and then apply is Opinion Lexicon dictionary[18] [19] and AFFIN polarity dictionary [20] to determine the polarity. ...
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... Belakangan, sentiment analysis menarik sebagian besar perhatian baik dari akademis maupun industri [1]- [5]. Hal ini disebabkan karena banyaknya masalah penelitian dan berbagai macam aplikasi yang menantang. ...
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Pada penelitian ini akan dibahas mengenai sebuah aplikasi yang dibuat secara khusus untuk mengkategorikan opini masyarakat terhadap sebuah berita Sepak Bola. Opini yang diolah diperoleh dari dua sumber, yaitu melalui hasil crawl situs berita olah raga dan opini yang ditambahkan oleh user sendiri pada aplikasi ini. Opini yang ada nantinya akan disajikan secara terpisah menurut kelompoknya; sentiment positive, negative, maupun netral. Proses klasifikasinya sendiri terdiri dari dua tahap. Tahap pertama adalah proses preprocessing yang terdiri atas proses tokenisasi, normalisasi, case folding, stop word removing, common word removing, stemming. Tahap kedua adalah mengklasifikasikan opini-opini tersebut dengan algoritma Baseline, dan Naive Bayes. Opini yang digunakan untuk proses klasifikasi yaitu opini yang menggunakan bahasa Inggris dari situs dan Dari perhitungan macroaveraged untuk setiap kelas, didapatkan akurasi 93,06%, presisi 81,90%, dan recall 92,67% untuk kelas sentiment positive. Dari perhitungan kelas sentiment negative didapatkan akurasi 87,73%, presisi 96,29%, dan recall 83,63%. Dari perhitungan kelas sentiment netral didapatkan akurasi 92,26%, presisi 64,44%, dan recall 90,37%. Kesimpulan yang diperoleh saat penelitian ini dari awal hingga akhir adalah, proses crawling yang digunakan untuk mendapatkan berita dan komentar berita sangat membantu dalam penambahan konten website, tetapi banyak sekali komentar berita yang diperoleh kurang cocok untuk proses klasifikasi.
... We validate our model on five text classification tasks. CR (Hu and Liu, 2004): Customer reviews composed of positive or negative product reviews; MR (Pang and Lee, 2004): Movie reviews divided into positive and negative categories; SUBJ: Subjectivity dataset where the target is to classify a text as being subjective or objective; MPQA (Wiebe et al., 2005): Opinion polarity detection subtask. 20NEWS: A international standard dataset for text classification, text mining, and information retrieval research. ...
... We evaluate our models on the following transfer tasks: MR (Pang and Lee, 2005), CR (Hu and Liu, 2004), SUBJ (Pang and Lee, 2004), MPQA (Wiebe et al., 2005), SST-2 (Socher et al., 2013), TREC (Voorhees and Tice, 2000) and MRPC (Dolan and Brockett, 2005). To this end, a logistic regression classifier is trained on top of (frozen) sentence embeddings produced by different methods. ...
... In 2003, semantic polarity (ISA) algorithm was devised by Turney et al. [25] to determine text sentiment tendency. Hu et al. [26] used WordNet seed words to create a vocabulary of negative and positive sentiment terms, and then classified sentences using the dictionary. Marquez et al. [27] proposed a technique for increasing the lexicon based on opinion. ...
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This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not rec- ommended (thumbs down). The classifi- cation of a review is predicted by the average semantic orientation of the phrases in the review that contain adjec- tives or adverbs. A phrase has a positive semantic orientation when it has good as- sociations (e.g., "subtle nuances") and a negative semantic orientation when it has bad associations (e.g., "very cavalier"). In this paper, the semantic orientation of a phrase is calculated as the mutual infor- mation between the given phrase and the word "excellent" minus the mutual information between the given phrase and the word "poor". A review is classified as recommended if the average semantic ori- entation of its phrases is positive. The al- gorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The ac- curacy ranges from 84% for automobile reviews to 66% for movie reviews.
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Current document-retrieval tools succeed in locating large numbers of documents relevant to a given query. While search results may be relevant according to the topic of the documents, it is more difficult to identify which of the relevant documents are most suitable for a particular user. Automatic genre analysis (i.e., the ability to distinguish documents according to style) would be a useful tool for identifying documents that are most suitable for a particular user. We investigate the use of machine learning for automatic genre classification. We introduce the idea of domain transfer—genre classifiers should be reusable across multiple topics—which does not arise in standard text classification. We investigate different features for building genre classifiers and their ability to transfer across multiple-topic domains. We also show how different feature-sets can be used in conjunction with each other to improve performance and reduce the number of documents that need to be labeled. © 2006 Wiley Periodicals, Inc.