The opinion seeking behavior of people for good decision making has greatly enhanced the importance of social media as a platform for exchange of information. This trend has led to a sudden spurt of information overflow on the Web. The huge volume of such information has to be technically processed for segregating the relevant knowledge. Sentiment analysis is the popular method extensively used for this purpose. It is defined as the computational study of mining the opinions from the available content about the entity of interest. Existing Sentiment analysis techniques quite efficiently capture opinions from text written in syntactically correct and explicit language. However, while dealing with the informal data, limitation has been observed in performance of sentiment analysis techniques. With a view to deal with the imperfect and indirect language used by the netizens, it has become necessary to work on improvement in the existing sentiment analysis techniques. In this regard, the conventional sentiment analysis techniques have shown some improvement on applying the appropriate context information. However, still there is ample scope for further research to find the relevant “context” and applying it to a given scenario. This systematic literature review paper intends to explore and analyze the existing work on the context-based sentiment analysis and to report gaps and future directions in the said research area.

Systematic literature review on context-based sentiment analysis in social multimedia

Sentiment lexicon is used to judge the sentiments of words and plays a significant role in sentiment analysis. Existing sentiment lexicons ignore the sentimental ambiguity of words in different contexts and only assign sentiment positive or negative polarity for words. In this paper, we propose an automatic method for the construction of the domain-specific sentiment lexicon (SDS-lex) to avoid sentimental ambiguity, which incorporates the sentiment information not only from the existing lexicons but also from the corpus by using our improved TF-IDF algorithm (ITF-IDF). The ITF-IDF algorithm calculates the sentiment of words by considering both the importance of words and the distribution of different part-of-speech (POS) in a corpus labeled with different sentiment tendencies. Experiments on real-world datasets show that our constructed lexicon improves the sentimental ambiguity and outperforms many existing lexicons in terms of the coverage and the accuracy when performing text sentiment classification tasks.

Automatic construction of domain sentiment lexicon for semantic disambiguation

In the Internet of Things (IoT) and Machine to Machine (M2M) environments, users seek a wide variety of services on their own mobile devices with limited resources, such as mobile phones and smart watches. Resource collaboration effectively addresses the concern of restricted resources. In our previous study, we employed reasoning time to recommend resources in our resource collaboration system. We found reasoning time increases exponentially depending on the number of resources and rules for filtering resources. To reduce reasoning time and improve the user satisfaction consequent to reasoning, this current research adopts and applies the Dominance, Inducement, Submission, and Compliance (DISC) model, a popular method in the psychological field for classifying characteristics on the basis of behavior patterns. By implementing and evaluating a prototype system, this paper shows the proposed method is a reasonable solution for resource recommendation.

Resource collaboration system based on user history and the psychological mode

Corpus Augmentation for Improving Neural Machine Translation

Affective computing study of attention recognition for the 3D guide system

With the explosive growth of various social media applications, individuals and organizations are increasingly using their contents (e.g. reviews, forum discussions, blogs, micro-blogs, comments, and postings in social network sites) for decision-making. These contents are typical big data. Opinion mining or sentiment analysis focuses on how to extract emotional semantics from these big data to help users to get a better decision. That is not an easy task, because it faces many problems, such as different context may make the meaning of the same word change variously, at the same time multilingual environment restricts the full use of the analysis results. Ontology provides knowledge about specific domains that are understandable by both the computers and developers. Building ontology is mainly a useful first step in providing and formalizing the semantics of information representation. We proposed an ontology DEMLOnto based on six basic emotions to help users to share existed information. The ont...

Wan Tao

Papers

Building ontology for different emotional contexts and multilingual environment in opinion mining