Sentiment analysis using product review data

Abstract

Sentiment analysis or opinion mining is one of the major tasks of NLP (Natural Language Processing). Sentiment analysis has gain much attention in recent years. In this paper, we aim to tackle the problem of sentiment polarity categorization, which is one of the fundamental problems of sentiment analysis. A general process for sentiment polarity categorization is proposed with detailed process descriptions. Data used in this study are online product reviews collected from Amazon.com. Experiments for both sentence-level categorization and review-level categorization are performed with promising outcomes. At last, we also give insight into our future work on sentiment analysis.

PROO ontology development for learning feature specific sentiment relationship rules on reviews categorisation: a semantic data mining approach

“…The parameters provided in Table 3 below compares the area covered under ROC curve between the works done by Fang and Zhang (2015) with the results obtained from the current work presented in Figure 8. They used Naïve Bayesian Classifier on the product features data for the task of sentiment analysis.…”

Sentiment Analysis: A Review

“…Authors Xing Fang, Justin Zhan [2] did experiments for two types of classification. First, sentence-level classification and review-level classification and also achieved good results.…”

Analysis on Hinglish Opinion Using Multinomial Naive Bayes Algorithm

“…Saif H., et.al. [3] In this paper, they provided an overview of eight publicly available and manually annotated evaluation datasets for Twitter sentiment analysis. They found that unlike the tweet level, very few annotation efforts were spent towards providing datasets for evaluating sentiment classifiers at the entity level.…”

“…Precision is calculated using equation (3). From the bar graph {1} it is clear that Multinomial Naïve Bayes algorithm gives better precision than Naïve Bayes algorithm.…”