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Master Thesis of sentiment Analysis [Last Edition]

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... Sentiment analysis is usually applied to reviews and social media. It calculates the aggregate sentiment polarity and classi es the sentiment as positive, neutral, or negative [43] In sentiment analysis, results are represented in score for each term as follows: positive score (s i + ), neutral score (s i 0 ) and negative score (s i − ). Each score is used to determine how that sentence is perceived [44]. ...
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Understanding human perception and requirements on nutrition for cancer prevention and condition management is important so that nutrition applications can be catered for cancer patients. In this paper, web-scraping was conducted to understand the public’s perception, attitude and requirements related to a plant-based diet as a recommended diet for cancer prevention and condition management. Text and sentiment analysis were carried out on results gathered from 73 social sites to determine whether non-cancer and cancer patients use plant-based diets, how they have been consumed, their benefits in the prevention and condition management of cancers, the existing myths/fake news about cancer and what do cancer patients need in a nutrition app. Results of the text analysis highlight missing gaps in existing apps to include a lack of credibility and endorsement by professionals. Future nutrition apps should provide personalized diet, symptoms management, good user experience, credibility, and emotional and mental health support.
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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
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Sentiment analysis or opinion mining has become an open research domain after proliferation of Internet and Web 2.0 social media. People express their attitudes and opinions on social media including blogs, discussion forums, tweets, etc. and, sentiment analysis concerns about detecting and extracting sentiment or opinion from online text. Sentiment based text classification is different from topical text classification since it involves discrimination based on expressed opinion on a topic. Feature selection is significant for sentiment analysis as the opinionated text may have high dimensions, which can adversely affect the performance of sentiment analysis classifier. This paper explores applicability of feature selection methods for sentiment analysis and investigates their performance for classification in term of recall, precision and accuracy. Five feature selection methods (Document Frequency, Information Gain, Gain Ratio, Chi Squared, and Relief-F) and three popular sentiment feature lexicons (HM, GI and Opinion Lexicon) are investigated on movie reviews corpus with a size of 2000 documents. The experimental results show that Information Gain gave consistent results and Gain Ratio performs overall best for sentimental feature selection while sentiment lexicons gave poor performance. Furthermore, we found that performance of the classifier depends on appropriate number of representative feature selected from text.
Chapter
Textual information in the world can be broadly categorized into two main types: facts and opinions. Facts are objective expressions about entities, events, and their properties. Opinions are usually subjective expressions that describe people’s sentiments, appraisals, or feelings toward entities, events, and their properties. The concept of opinion is very broad. In this chapter, we only focus on opinion expressions that convey people’s positive or negative sentiments. Much of the existing research on textual information processing has been focused on themining and retrieval of factual information, e.g., information retrieval (IR), Web search, text classification, text clustering, and many other text mining and natural language processing tasks. Littleworkhadbeendone on the processing of opinions until only recently. Yet, opinions are so important that whenever we need to make a decision we want to hear others’ opinions. This is not only true for individuals but also true for organizations.
Article
With the rapid growth of social media, sentiment analysis, also called opinion mining, has become one of the most active research areas in natural language processing. Its application is also widespread, from business services to political campaigns. This article gives an introduction to this important area and presents some recent developments.
Conference Paper
Research on Sentiment Analysis (SA) has increased tremendously in recent times due to fast growth in Web Technologies. Hindi Language content is also growing very fast online. Sentiment classification research has been done mostly for English language. However, there has been little work in this area for Indian languages. Sentiment analysis means to extract the opinion expressed in the text about a specific topic. There is a need to analyse the Hindi language content and get insight of opinions expressed by people and various communities about a specific topic. In this paper, it is investigated that how by proper handling of negation and discourse relation may improve the performance of Hindi review sentiment analysis. Experimental results show the effectiveness of the proposed approach.
Conference Paper
The high popularity of modern web is partly due to the increase in the number of content sharing applications. The social tools provided by the content sharing applications allow online users to interact, to express their opinions and to read opinions from other users. However, spammers provide comments which are written intentionally to mislead users by redirecting them to web sites to increase their rating and to promote products less known on the market. Reading spam comments is a bad experience and a waste of time for most of the online users but can also be harming and cause damage to the reader. Research has been performed in this domain in order to identify and eliminate spam comments. Our goal is to detect comments which are likely to represent spam considering some indicators: a discontinuous text flow, inadequate and vulgar language or not related to a specific context. Our approach relies on machine learning algorithms and topic detection.