Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification

High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions

The Today Tendency of Sentiment Classification

Sentiment classification has already been studied for many years because it has had many crucial contributions to many different fields in everyday life, such as in political activities, commodity production, and commercial activities. There have been many kinds of the sentiment analysis such as machine learning approaches, lexicon-based approaches, etc., for many years. The today tendency of the sentiment classification is as follows: (1) Processing many big data sets with shortening execution times (2) Having a high accuracy (3) Integrating flexibly and easily into many small machines or many different approaches. We will present each category in more details

Sentiment Analysis or Opinion Mining: A Review

Opinion Mining (OM) or Sentiment Analysis (SA) can be defined as the task of detecting, extracting and classifying opinions on something. It is a type of the processing of the natural language (NLP) to track the public mood to a certain law, policy, or marketing, etc. It involves a way that development for the collection and examination of comments and opinions about legislation, laws, policies, etc., which are posted on the social media. The process of information extraction is very important because it is a very useful technique but also a challenging task. That mean, to extract sentiment from an object in the web-wide, need to automate opinion-mining systems to do it. The existing techniques for sentiment analysis include machine learning (supervised and unsupervised), and lexical-based approaches. Hence, the main aim of this paper presents a survey of sentiment analysis (SA) and opinion mining (OM) approaches, various techniques used that related in this field. As well, it discusses the application areas and challenges for sentiment analysis with insight into the past researcher's works

Research on Automatic Identification of Rumors in Stock Forum Based on Machine Learning

When rumors prevail in securities market, it is very difficult for investors to identify valid information. In the meantime, investors have much more ways to access information with the evolution of internet. But there is an overwhelming quantity of information on the Internet, the coexistence of facts and rumors, namely, “widely circulated” and “specious”, yet “unconfirmed officially” vague information, makes it more difficult for investors who with limited rationality to distinguish facts from rumors. Existing studies are mainly devoted in the method of event study, namely screening rumors from “official channels” that clarified, which is neither timely efficient in terms of accessing to rumors nor providing the basis for decision-making. Traditional news has evolved into various forms of social media, including forums, blogs, micro-blogs etc., and users can not only gain quick access to more valuable and timely information, but also amplify information that embed the news effectively by participating in commenting on various social media. Dynamic information creation, sharing and coordination among Web users are exerting increasingly prominent impact on the securities market in now days. Thus, it is very necessary to study the effects of social media as online forums on the securities market. In this paper, the method of machine learning is adopted for the first time to identifying the Internet rumors automatically, and successfully in crawling massive forum data by smart computer technology. Unlike the case study and statistical sampling of rumors, this paper conduct automatic identification of Internet rumors by utilize the smart technology, thus paving the way for more in-depth analysis about the effects of Internet media on the securities market in future

An Information Diffusion-Based Recommendation Framework for Micro-Blogging

Micro-blogging is increasingly evolving from a daily chatting tool into a critical platform for individuals and organizations to seek and share real-time news updates during emergencies. However, seeking and extracting useful information from micro-blogging sites poses significant challenges due to the volume of the traffic and the presence of a large body of irrelevant personal messages and spam. In this paper, we propose a novel recommendation framework to overcome this problem. By analyzing information diffusion patterns among a large set of micro-blogs that play the role of emergency news providers, our approach selects a small subset as recommended emergency news feeds for regular users. We evaluate our diffusion-based recommendation framework on Twitter during the early outbreak of H1N1 Flu. The evaluation results show that our method results in more balanced and comprehensive recommendations compared to benchmark approaches

A META-ANALYTIC REVIEW OF SOCIAL MEDIA STUDIES

Social media such as social networking sites, blogs, micro-blogs, Wikis, are increasingly and widely used in our daily lives. In the information system (IS) discipline, social media have become a hot research area and draw the attention of many scholars. The paper systematically reviewed social media studies published in Association for Information Systems (AIS) listed top 20 journals from 2009 to 2013. The publication time, journal preferences, research objects and research topics are discussed. Generally, the current social media studies including four areas, namely user, management, technology and information. Each area has distinct focuses and topics. By thoroughly analyzing the research topics, the authors formulate our projections and recommendations for future social media studies