IIIT Hyderabad Publications |
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Sentiment Analysis and Opinion Mining from Noisy Social Media ContentAuthor: Aditya Joshi Date: 2019-04-17 Report no: IIIT/TH/2019/62 Advisor:Vasudeva Varma,Manish Shrivastava AbstractUser generated content, mainly through social media, blogs and review websites etc. offers a powerful outlet for peoples thoughts and feelings. This presents an enormous ever-growing source of texts ranging from everyday observations to involved discussions. This thesis approaches problems of sentiment analysis from recently popularized perspectives. We explore how sentiment of the user generated text can be computed using a combination of heuristics and machine learning approaches. This thesis contributes to the field of sentiment analysis, which aims to extract emotions and opinions from text. Social Media and other mediums of user generated content provide a powerful source for aggregating peoples’ thoughts and feelings towards a particular entity, event or topic. A robust sentiment analysis system is helpful for the business entities towards which the sentiment is expressed, providing them a solution to understand users’ perception towards the product and services they offer. It is also helpful for other users in form of summary of reviews about a product and service the user wishes to pay for. There are hundreds of websites providing opinionated user-generated content, it is difficult to go through all and then make some decision. Automated sentiment detection helps identify pros and cons of a product with ease. Sentiment analysis system presents a reliable tool for decision making for various parties. This thesis begins with sentiment analysis on social media using machine learning methods. Various signals were specifically extracted for social media setting. A method for fine grain sentiment analysis is presented using a sequence labelling approach combined with heuristics for improved features. To further enhance the sentiment analysis on social media, a solution is proposed for sentiment analysis of Code Mixed data, which is otherwise treated as noise and thus huge amount of information is lost. We conclude that feature engineering for standard machine learning methods and deep learning provide a reliable solution for sentiment analysis. Full thesis: pdf Centre for Search and Information Extraction Lab |
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