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Twitter Sentimental Analysis
Subject area: Science,Engineering and Technology · Area of research: IOT
Abstract
This paper address the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analyzing the sentiments expressed in the tweets. Analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.
Keywords
Twitter, Social Media, Analysis, Sentiment
References
[1] Albert Biffet and Eibe Frank. Sentiment Knowledge Discovery in Twitter Streaming Data. Discovery Science, Lecture Notes in Computer Science,2010, Volume 6332/2010, 1- 15,
[2] Alec Go, Richa Bhayani and Lei Huang. Twitter Sentiment Classification using Distant Supervision. Project Technical Report, Stanford University,2009.
[3] Alexander Pak and Patrick Paroubek. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. In Proceedings of international conference on Language Resources and Evaluation (LREC),2010.
[4] Andranik Tumasjan, Timm O. Sprenger, Philipp G. Sandner and Isabell M. Welpe. Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. In Proceedings of AAAI Conference on Weblogs and Social Media (ICWSM),2010.
[5] Bo Pang, Lillian Lee and Shivakumar Vaithyanathan. Thumbs up? Sentiment Classification using Machine Learning Techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP),2002.
[6] Chenhao Tan, Lilian Lee, Jie Tang, Long Jiang, Ming Zhou and Ping Li. User Level Sentiment Analysis Incorporating Social Networks. In Proceedings of ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), 2011.
[7] Efthymios Kouloumpis, Theresa Wilson and Johanna Moore. Twitter Sentiment Analysis: The Good the Bad and the OMG! In Proceedings of AAAI Conference on Weblogs and Social Media (ICWSM),2011.
How to cite this paper
@article{1702663,
author = {Sahil Kadam, Ishani Shirsat, Akash Dwivedi, Dr. Savita Sangam},
title = {Twitter Sentimental Analysis},
journal = {Iconic Research And Engineering Journals},
year = {2021},
volume = {4},
number = {10},
pages = {71-75},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1702663.pdf},
abstract = {This paper address the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analyzing the sentiments expressed in the tweets. Analyzing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.},
keywords = {Twitter, Social Media, Analysis, Sentiment},
month = {April},
}