Topic-Aware Sentiment Prediction for Chinese ConceptNet
| Autor: | Po-Hao Chou, 周柏豪 |
|---|---|
| Rok vydání: | 2015 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 103 ConceptNet is a semantic network which stores general knowledge into more computable representations. In ConceptNet, the notion of a node is ex- tended from purely lexical terms to include higher-order compound concepts, e.g., ’eat lunch’, ’satisfy hunger’, and these nodes are called concepts. There may be a directed edge connecting two concepts. Each directed edge is as- sociated with one of the predefined relation types to represent the semantic relation between two concepts it connects, e.g., ’CapableOf’, ’Causes’. These directed edges are called relations. Sentiment analysis aims to identify the attitudes or emotions behind texts. For most approaches, sentiment information of terms or phrases is an im- portant resource. However, in Chinese sentiment analysis, the coverage of available resources is still limited. To increase the coverage, some methods has been developed to predict sentiments for nodes in Chinese ConceptNet due to its large size and high semantic level nodes. Current approaches aim to assign one sentiment to each concept, but in fact a concept may have different sentiments on different contexts, such as ’ 尖叫’ and ’ 突然’. In this thesis, we aim to extract the hidden contextual information in Chinese ConceptNet and use it to estimate sentiments in different situations for each concept. To achieve this goal, we design a topic-aware sentiment propagation system. We propose using Latent Dirichlet Allocation to divide Chinese ConceptNet into different topic layers. On each topic layer we perform sentiment propaga- tion through some types of relations to predict sentiments on the topic for concepts. Our another goal is to use the generated topic-aware sentiments of concepts to improve the polarity classification for texts. We propose combin- ing other co-occurring concepts to identify topics and select proper sentiments for concepts in texts. This thesis conducts three experiments to investigate the effect of topic-aware sentiment prediction. The results show that extracting topic is helpful in both predicting sentiment values of concepts and predicting polarities of our test texts. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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