Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/229657
Title: Holistic Multilingual Sentiment Analysis on Reviews in Social Media
Researcher: Kaur, Sukhnandan
Guide(s): Mohana, Rajni
Keywords: Emoticons
Engineering and Technology,Computer Science,Computer Science Information Systems
Macaronic
Sentiment Analysis
Sentiscore
University: Jaypee University of Information Technology, Solan
Completed Date: 2019
Abstract: Decades ago, people used to represent their opinion by writing it manually or by speaking at public places. These reviews are further taken as an advice for the betterment. To process this data was a complete manual task. As the usage of internet grew, people started sharing their views regarding any entity through emails or social platforms. newlineThe intensification of data over the social media makes the task of deducing valuable information a bit complex. Automatic deduction of sentiments from web data is considered as a process of sentiment analysis. An algorithm devised for the same is known as sentiment analyser. Use of Sentiment analysers is at the peak for various enterprises to find the loopholes in their product or services. An optimal sentiment analyser is the one which works rationally as humans. The goal is thus to fill the research gaps associated with the effective sentiment processing. newlineSentiment analysis integrates many subtasks i.e. Named Entity Extraction, Anaphora resolution, Sentiscore, Feature extraction, etc. Effective pre-processing yields better results for all the natural language processing tasks. The reason for pre-processing of the data is that people use slangs, long tail words, multilingual content and visual language such as emoticons. People use unstructured format of writing along with all the above mentioned categories these days. The process to handle slangs, misspelled words, etc. is called as normalization. newlineThe primary aim of this study is to have effective pre-processing of the content i.e. normalization. Normalization here deals with two aspects: one is to deal with slangs and another is to deal with emoticons. In this study, a technique is used where each emoticon is mapped corresponding to its meaning for generating Sentiscore, instead of just adding or subtracting one for positive and negative smiley respectively. Slangs are also handled effectively by using cross word dictionary and corpus. It is aimed to get better results for pre-processing. newline
Pagination: xiv, 142p.
URI: http://hdl.handle.net/10603/229657
Appears in Departments:Department of Computer Science Engineering

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01_title.pdfAttached File195.54 kBAdobe PDFView/Open
02_certificate;declaration;acknowledgement.pdf578.4 kBAdobe PDFView/Open
03_table of contents;list of tables & figures;abbreviations.pdf307.24 kBAdobe PDFView/Open
04_abstract.pdf87.47 kBAdobe PDFView/Open
05_chapter 1.pdf373.71 kBAdobe PDFView/Open
06_chapter 2.pdf357.41 kBAdobe PDFView/Open
07_chapter 3.pdf438.84 kBAdobe PDFView/Open
08_chapter 4.pdf580.94 kBAdobe PDFView/Open
09_chapter 5.pdf355.47 kBAdobe PDFView/Open
10_conclusions.pdf95.09 kBAdobe PDFView/Open
11_bibliography.pdf165.92 kBAdobe PDFView/Open


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