Recognizing sentiment of relations between entities in text

Abstract

Recently, sentiment analysis for identifying positive or negative opinions from texts has received much attention. In this paper, we introduce sentiment analysis into a new field, which recognizes sentiment of relations between entities in the text. Three sentiment polarities between entities are recognized, namely positive, negative, and neutral. The difficulty in this work is that several pairs of entities may appear in the same sentence, and their sentiment polarities are determined by different related regions of the sentence. In addition, different features of words and their interactions in a related region will affect the final sentiment. It is difficult to process this using rigid rule‐based methods. Therefore, we propose a machine‐learning method based on statistics. In the proposed method, the model of conditional random fields (CRFs) is used to annotate the sentiment polarity between entities with the help of the syntactic dependency tree. The string of words that connects two entities in the dependency tree is used as the related region to recognize the sentiment. Experimental results and comparison with the other methods based on different principles and related regions suggest that the proposed method shows better performance and proves its validity. Moreover, the effect of different features on the CRF is word of the ith word in the sentence. We can get a CRF model using the CRF++ tool based on feature template and training corpus. After obtaining the model, sentiment of relations could be assigned automatically. The algorithm is shown in Fig. 2. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

A Survey of Signed Network Mining in Social Media

“…A typical scenario of the application of the voter model is when users' opinions switch forth and back according to their interactions with other users in networks. The authors of [Li et al 2013;Li et al 2014] investigate how two opposite opinions diffuse in signed networks based on the voter model proposed in [Clifford and Sudbury 1973]. It is more likely for users to adopt and trust opinions from their friends, while users are likely to adopt the opposite opinions of their foes.…”

Trends detection of flu based on ensemble models with emotional factors from social networks

“…In order to make comparisons, we also revised the CRFs to detect the flu state of a single person and construct a supervised flu state detection model . The posts of a flu infected person can be treated as a sequence, and the flu state detection of the person can be treated as a sequence labeling problem, which can be solved by CRFs .…”