Predicting Election Results using NLTK
Kambhampati Kalyana Kameswari1, J Raghaveni, R. Shiva Shankar2, Ch. Someswara Rao3
1Kambhampati Kalyana Kameswari, M. tech Student, Department of CSE, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
2J Raghaveni, Assistant Professor of Computer Science and Engineering, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
3R.Shiva Shankar, Assistant Professor of Computer Science and Engineering, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
4Ch. Someswara Rao, Assistant Professor of Computer Science and Engineering, SRKR Engineering College affiliated to JNTU Kakinada, Bhimavaram, AP, India.
Manuscript received on October 13, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 4519-4529 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4399119119/2019©BEIESP | DOI: 10.35940/ijitee.A4399.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world, people are usually using social media networks for trying to communicate with other users and for sharing information across the world. The online social networking sites have become considerable tools and are providing a common medium for a number of users to communicate with each other. Twitter is the most prominent microblogging website and one among the social networking sites that grow on a daily basis. Social media incorporates an extensive amount of data in the form of tweets, forums, status updates, comments, etc. in an attempt to automatically process and analyze these data, applications can rely on analysis approaches such as sentiment analysis. Twitter sentiment analysis is an application of sentiment analysis on data from Twitter (tweets), to obtain user’s opinions and sentiments. Natural Language Toolkit (NLTK) is a library based on machine learning methods in python & sentiment analysis tool. Which provides the base for text processing and classification? The research work proposed a machine learning-based classifier to extract the tweets on elections and analyze the opinion of the tweeples (people who use twitter). The tweets can be categorized as positive, negative and neutral towards a particular politician. We classify these processed tweets using a supervised machine learning classification approach. The classifier used to classify the tweets as positive, negative or neutral is Naive Bayes Classifier. The classifier is trained with tweets bearing a distinctive polarity. The percentage of positive and negative tweets is then measured and graphically represented.
Keywords: Machine Learning, Natural Language Processing, Twitter, Political opinion, Supervised Learning, Election Result Prediction.
Scope of the Article: Machine Learning