SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUES
Author(s):
arpita lasod , rahul pawar
ISSN:
2349-6002
Cite This Article:
SENTIMENT ANALYSIS USING MACHINE LEARNING TECHNIQUESInternational Journal of Innovative Research in Technology(www.ijirt.org) ,ISSN: 2349-6002 ,Volume 6 ,Issue 7 ,Page(s):153-157 ,December 2019 ,Available :IJIRT148884_PAPER.pdf
Keywords:
Abstract
Sentiment Analysis (SA) is a task of identifying positive and negative opinions; emotion and evaluation in text available over the social networking sites and the World Wide Web have been gained quite popularity in the recent years. The analysis serves as an important feedback for further improvement in the offered services and user experiences. Several techniques have been used recently including machine learning approaches and vocabulary orientated semantic algorithms. This report presents a survey of various techniques and tools have been used in the previous research sentiment analysis process. There is a massive increase in number of people who access various social networking and micro-blogging websites that gives new shapes the impression of today’s generation. Many reviews for a specific product, brand, individual, and movies etc. are helpful in directing the perception of people thus the analysts are begun to create algorithms to automate the classification of distinctive reviews on the basis of their polarities in particular : Positive, Analysis. The ultimate aim of this paper is to apply support vector machine (SVM) classification technique to classify the sentiments of smart phone product review which analyses datasets used for classification of sentiments and texts. Also, data sets are used for training as well as testing and implemented through SVM technique for finding the polarity of the ambiguous tweets. The obtain results show to achieve high accuracy as predicted on the basis of reviews of smart phone.
Article Details
Unique Paper ID: 148884

Publication Volume & Issue: Volume 6, Issue 7

Page(s): 153 - 157
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Last Date 25 January 2020


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