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A Sentiment Analysis of a Boycott Movement on Twitter
... Since sentiment analysis merely report polarity of text content whether positive, negative, or neutral, few prior studies analyzed emotions expressed by the authors in the text content [3,11]. Hence, this study also examined the emotions expressed in each tweet using text2emotion Python Library. ...
Background and aims
The COVID-19 pandemic outbreak has created severe public health crises and economic consequences across the globe. This study used text analytics techniques to investigate the key concerns of Indian citizens raised in social media during the second wave of COVID-19.
Methods
In this study, we performed a sentiment and emotion analysis of tweets to understand the attitude of Indian citizens during the second wave of COVID-19. Moreover, we performed topic modeling to understand the significant issues and concerns related to COVID-19.
Results
Our results show that most social media posts were in neutral tone, and the percentage of posts that showed positive sentiment was less. Furthermore, emotion analysis results show that ‘Fear’ and ‘Surprise’ were the prominent emotions expressed by the citizens. Topic modeling results reveal that ‘High crowd’ and ‘political rallies’ are the two primary topics of concern raised by Indian citizens during the second wave of COVID-19.
Conclusions
Hence, Indian government agencies should communicate crisis information and combating strategies to citizens more effectively in order to minimize the fear and anxiety amongst the public.
Social media especially Twitter is providing a space for expression and
opinions, where users discuss various events, services, and brands. Entrepreneurs are
in continuous need of the feedback about their services to improve the quality and
quantity. However, due to the bulk amount of data, it’s difficult to detect the
consumer’s opinions. This article deliberates the problems about the Twitter data for
the sentiment analysis. Furthermore, it implements the text mining and document-
based sentiment on the preprocessed Twitter data through the machine learning
techniques, Naïve Bayes and lexicon dictionary. Our case study is to find the public
opinion about the top two apparel international brands and compare the positive and
negative attitude of common users about each brand. We found that positive reviews
of Adidas are more than the Nike while there is the slight difference in negative
reviews. It founds that people want to discuss the other brands as comparisons when
they are talking about just one brand.
Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to
keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6,996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software development and cyberbullying extend the utilization of sentiment analysis.
Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers' perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers' perceptions. This paper reports on the design of a sentiment analysis, extracting a vast amount of tweets. Prototyping is used in this development. Results classify customers' perspective via tweets into positive and negative, which is represented in a pie chart and html page. However, the program has planned to develop on a web application system, but due to limitation of Django which can be worked on a Linux server or LAMP, for further this approach need to be done.
With the advancement of web technology and its growth, there is a huge volume of data present in the web for internet users and a lot of data is generated too. Internet has become a platform for online learning, exchanging ideas and sharing opinions. Social networking sites like Twitter, Facebook, Google+ are rapidly gaining popularity as they allow people to share and express their views about topics,have discussion with different communities, or post messages across the world. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyze the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases. In this paper, we provide a survey and a comparative analyses of existing techniques for opinion mining like machine learning and lexicon-based approaches, together with evaluation metrics. Using various machine learning algorithms like Naive Bayes, Max Entropy, and Support Vector Machine, we provide a research on twitter data streams.General challenges and applications of Sentiment Analysis on Twitter are also discussed in this paper.
We examine sentiment analysis on Twitter data. The contributions of this paper are: (1) We introduce POS-specific prior polarity fea- tures. (2) We explore the use of a tree kernel to obviate the need for tedious feature engineer- ing. The new features (in conjunction with previously proposed features) and the tree ker- nel perform approximately at the same level, both outperforming the state-of-the-art base- line. kernel based model. For the feature based model we use some of the features proposed in past liter- ature and propose new features. For the tree ker- nel based model we design a new tree representa- tion for tweets. We use a unigram model, previously shown to work well for sentiment analysis for Twit- ter data, as our baseline. Our experiments show that a unigram model is indeed a hard baseline achieving over 20% over the chance baseline for both classifi- cation tasks. Our feature based model that uses only 100 features achieves similar accuracy as the uni- gram model that uses over 10,000 features. Our tree kernel based model outperforms both these models by a significant margin. We also experiment with a combination of models: combining unigrams with our features and combining our features with the tree kernel. Both these combinations outperform the un- igram baseline by over 4% for both classification tasks. In this paper, we present extensive feature analysis of the 100 features we propose. Our ex- periments show that features that have to do with Twitter-specific features (emoticons, hashtags etc.) add value to the classifier but only marginally. Fea- tures that combine prior polarity of words with their parts-of-speech tags are most important for both the classification tasks. Thus, we see that standard nat- ural language processing tools are useful even in a genre which is quite different from the genre on which they were trained (newswire). Furthermore, we also show that the tree kernel model performs roughly as well as the best feature based models, even though it does not require detailed feature en-
Sentiment analysis or Opinion Mining or Emotion Artificial Intelligence is an on-going field which refers to the use of Natural Language Processing, analysis of text and is utilized to extract quantify and is used to study the emotional states from a given piece of information or text data set. It is an area that continues to be currently in progress in field of text mining. Sentiment analysis is utilized in many corporations for review of products, comments from social media and from a small amount of it is utilized to check whether or not the text is positive, negative or neutral. Throughout this research work we wish to adopt rule- based approaches which defines a set of rules and inputs like Classic Natural Language Processing techniques, stemming, tokenization, a region of speech tagging and parsing of machine learning for sentiment analysis which is going to be implemented by most advanced python language.
Social networks are the main resources to gather information about people's opinion and sentiments towards different topics as they spend hours daily on social media and share their opinion. In this technical paper, we show the application of sentimental analysis and how to connect to Twitter and run sentimental analysis queries. We run experiments on different queries from politics to humanity and show the interesting results. We realized that the neutral sentiments for tweets are significantly high which clearly shows the limitations of the current works.
With the rise of social networking epoch, there has been a
surge of user generated content. Microblogging sites have
millions of people sharing their thoughts daily because of
its characteristic short and simple manner of expression.
We propose and investigate a paradigm to mine the
sentiment from a popular real-time microblogging service,
Twitter, where users post real time reactions to and
opinions about “everything”. In this paper, we expound a
hybrid approach using both corpus based and dictionary
based methods to determine the semantic orientation of the
opinion words in tweets. A case study is presented to
illustrate the use and effectiveness of the proposed system.
Sentiment analysis on Twitter, data for product evaluation
- S Sirsat
- S Rao
- B Wukkadada
Opinion mining on Twitter data
- S S Harikantra
- R Fernandes
Twitter sentiment analysis
- Faizan
Sentiment research on Twitter data
- A Brahmananda Reddy
- D N Vasundhara
- P Subhash
Twitter sentiment analysis: a case study for apparel brands
- A Rasool
- R Tao
- K Marjan
- T Naveed