Name
Abstract | ||
|---|---|---|
Sentiment analysis has important applications in many areas, including marketing, recommendation, and financial analysis. Since topic modeling can discover hidden semantic structures, researchers put forward sentiment analysis models based on topic models. These models have been successfully applied on long texts, but analysis for short text is a challenging task because of the sparsity of features in short texts. We observe that the textual context has been widely considered on text analysis task, but on sentiment analysis area, most sentiment analysis models still lack of consideration and integration of sentimental context. Thus, by taking the speciality of sentiment analysis task and short text into consideration, we propose the sentimental context to enrich the characteristics and improve the performance of sentiment classification over short text. We first put forward the concept of sentimental context, which is extracted from the text body and sentiment lexicon, and then we integrate the sentimental context and propose two sentiment classification models based on word-level and topic-level respectively. We present results on real-world datasets from various sources, validating the effectiveness of the proposed models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/BESC.2017.8256405 | 2017 International Conference on Behavioral, Economic, Socio-cultural Computing (BESC) |
Keywords | Field | DocType |
text analysis task,sentiment analysis models,sentimental context,sentiment analysis task,short text,sentiment lexicon,sentiment classification models,financial analysis | Text mining,Sentiment analysis,Computer science,Financial analysis,Context model,Lexicon,Artificial intelligence,Natural language processing,Topic model,Semantics | Conference |
ISBN | Citations | PageRank |
978-1-5386-2367-1 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wenjie Zheng | 1 | 0 | 0.34 |
| Zenan Xu | 2 | 0 | 1.35 |
| Yanghui Rao | 3 | 256 | 23.32 |
| Haoran Xie | 4 | 450 | 71.21 |
| Fu Lee Wang | 5 | 926 | 118.55 |
| R. Kwan | 6 | 2 | 1.06 |