A Sentimental Analysis of Movie Reviews Involving Fuzzy Rule-Based

International Journal of Artificial Intelligence and Knowledge Discovery

Amit Purushottam Pimpalkar¹ 

[1] Shri Ram Institute of Technology, Jabalpur, RGPV, Bhopal

关键词: SentiWordNet;    Natural Language Processing;    Machine Learning;    Movie Review;    Sentiment Analysis System;    Web Opinion Mining;    Fuzzy measures;    Text Tokenization.;   

DOI  :  

学科分类：建筑学

来源: RG Education Society

【 摘 要 】

With the express development of online resources, discussion forums, groups and blogs; people communicate through these means of internet on daily basis. Today, enormous amounts of subjective text are available on the internet. Business forecasters are revolving their eyes on the internet in order to obtain realistic and subjective information (opinions) for their companies and products. Opinion mining can aid in a number of potential applications in areas such as search engines, market research and recommender systems.Sentiment Analysis (SA) is extensive contribution of Natural Language Processing (NLP) which covenant with the computational measures of opinion, sentiment, subjectivity and objectivity in the given text. SA is the process of extracting intellectual capacity from the people’s judgment, assessment and emotions toward an entities, an event and their attributes. These opinions significantly make impact on consumers to take their preference regarding shopping, choosing products and entities. As a result, it is desired to develop an efficient and effective SA system for customer reviews and comments. We consider the quandary of determining the polarity of sentiments in reviews when negation words occur in the sentences.This project usesSentiWordNetto assigns sentiment scores to each word found in comments. Sentiments of the words are assigned three sentiment scores: Positivity, Negativity and Objectivity with a word which lies in between the range from 0 to 1. The project also uses Rule-Based Fuzzy measures Approach and gives the output.

【 授权许可】

Unknown