The emergence of social media and the web has lead to the sheer volume of resources through the various discussion forums, blogs and media websites. The users are thus striving hard to understand and discover huge amount of reviews over the web and thus researches in sentimental analysis is growing rapidly. It becomes impossible to manually analyze the reviews and it became a motivation for effective opinion mining. To overcome such problem there is a need for effective algorithms to be used in the predicting the sentiments that paves the way for important decision making for the users and the manufacturers in the feedback of their product. This paper provides the survey about the challenges and overview of some classification and clustering algorithms used for sentimental analysis and opinion mining.

Algorithms for Opinion Mining and Sentiment Analysis: An Overview

Opinion mining and its applications in product recommendation, business intelligence, targeted marketing etc. has got a lot of research attention to the area of data mining. Many researches have been conducted for improving opinion mining and various frameworks have been proposed. Still the improvement is in progress and more efficient and improved frameworks are being proposed. For opinion analysis from reviews, classifying them as positive and negative and then making future decisions about the product is very important and fascinating aspect of text mining. Many techniques fail to provide the coverage to the features of the product and are not very progressive in accurately classifying and ranking the reviews. In this paper, we have proposed a framework for opinion mining to process public reviews in Facebook comments. The features are ranked and clustered according to the similarity between them. Many methodologies fail at the point of finding which features are relatively similar and can be easily grouped together. This framework also retrieves reviews and summarizes them in a most efficient way providing coverage to the features.

A Framework for Feature Extraction and Ranking for Opinion Making from Online Reviews

Opinion Mining has been a field of great interest and use as it helps the producers to know about the reviews of the product so as to enhance their sales. At the same time it helps the consumers as well to judge which product suits their requirements and whether the overall feedback from other consumers who have used it is positive or negative. This research proposes a system which aims to help the customers deal with bulk of reviews in an easier manner. The research also comprehends the optimum classifier to judge the contextual polarity of the review.

/pdf/review-of-classifiers-for-automated-opinion-mining-5rlc6kv8l5.pdf

Review of Classifiers for Automated Opinion Mining

Electronic mail being one of the most prevalent methods for exchange of digital messages has been facing the biggest threat that is spam. Since the text spam can be easily detected so a new variant of spamming came into being. The new variant of spam is the image spam that is the trending method of spamming. This paper investigates the several classifiers used in the image spam classification like the Decision Tree and the Support Vector Machine (SVM) and a combination of the both.

/pdf/filtering-image-spam-a-survey-3eadzb2wvn.pdf

Shina Shina

Papers

Review of Classifiers for Automated Opinion Mining

Filtering Image Spam: A Survey