Opinion Mining Framework Research Articles

Opinion Mining Framework in the Education Domain

Opinion Mining Framework in the Education Domain

An opinion mining framework for Cantonese reviews

Many research works are concentrating in how to handle the opinion mining for English, Chinese and Japanese, etc. However, little work has been done on opinion mining for Cantonese which is a world-wide influential language with 70 million speakers. In this paper, we point out that instead of utilizing the Mandarin lexical database, it’s necessary to construct a particular lexical database for Cantonese. Besides, we explore some Cantonese special written-tradition rules and incorporate them into the feature-based opinion summarization system framework. The experimental results show that our framework significantly outperform the traditional Mandarin sentiment analysis method using ICTCLAS.

Measuring the Influence of Mass Media on Opinion Segregation through Twitter

During the US presidential elections the media played a major role in presenting the candidates’ vision on several topics. Nevertheless, the diversity of opinions along with the political currents, one might notice segregation in opinions among some topics related to each other or a candidate. In the meanwhile, posting opinions on social media could be represented as a sentiment vector towards multiple issues. This gives us a ripe ground for clustering opinions to view tweets that hold similar opinions. In this paper we investigate the media's influence on segregating opinions by constructing an aspect-based opinion mining framework. Our main task is to detect the segregated groups of opinions by solving the proposed model using expectation maximization (EM) algorithm. We examined a corpus of tweets collected, which are related to famous political topics. We show interesting observations on the sentiment used for particular topics among the groups of opinions, and conclude the percentages of media influences among the segregated groups of opinions with respect to these topics.

TOM: Twitter opinion mining framework using hybrid classification scheme

Twitter has become one of the most popular micro-blogging platform recently. Millions of users can share their thoughts and opinions about different aspects and events on the micro-blogging platform. Therefore, Twitter is considered as a rich source of information for decision making and sentiment analysis. Sentiment analysis refers to a classification problem where the main focus is to predict the polarity of words and then classify them into positive and negative feelings with the aim of identifying attitude and opinions that are expressed in any form or language. Sentiment analysis over Twitter offers organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. The primary issues in previous techniques are classification accuracy, data sparsity and sarcasm, as they incorrectly classify most of the tweets with a very high percentage of tweets incorrectly classified as neutral. This research paper focuses on these problems and presents an algorithm for twitter feeds classification based on a hybrid approach. The proposed method includes various pre-processing steps before feeding the text to the classifier. Experimental results show that the proposed technique overcomes the previous limitations and achieves higher accuracy when compared to similar techniques.

A Framework for Opinion Mining in Blogs for Agriculture

In recent years there is much talk about blogging and the way in which blogs influence media and change the way people communicate and share knowledge. Blogs are also at the center of attention commercially, while a large number of academic staff researches on them. Furthermore, blogs represent an important new arena in agriculture sector for knowledge discovery since farmers use them for professional reasons. Opinion mining is a kind of text mining. Its goal is to assess the attitude of the author with respect to a given subject. The attitude may be a positive or negative opinion. This paper outlines the challenges and opportunities of the blogs for agriculture in terms of analyzing the information which is stored in them. The used technique in an experiment blog with the aid of the RapidMiner software is opinion mining. This framework may thus help establish baselines for these opinion mining tasks in agriculture.

Towards a unified framework for opinion retrieval, mining and summarization

The exponential increase of subjective, user-generated content since the birth of the Social Web, has led to the necessity of developing automatic text processing systems able to extract, process and present relevant knowledge. In this paper, we tackle the Opinion Retrieval, Mining and Summarization task, by proposing a unified framework, composed of three crucial components (information retrieval, opinion mining and text summarization) that allow the retrieval, classification and summarization of subjective information. An extensive analysis is conducted, where different configurations of the framework are suggested and analyzed, in order to determine which is the best one, and under which conditions. The evaluation carried out and the results obtained show the appropriateness of the individual components, as well as the framework as a whole. By achieving an improvement over 10% compared to the state-of-the-art approaches in the context of blogs, we can conclude that subjective text can be efficiently dealt with by means of our proposed framework.