
Abstract Micro-blog texts contain complex and abundant sentiments which reflect user’s standpoints or opinions on a given topic. However, the existing classification method of sentiments cannot facilitate micro-blog topic monitoring. To solve this problem, this paper presents a sentiment analysis method for Chinese micro-blog text based on the sentiment dictionary to support network regulators’ work better. First, the sentiment dictionary can be extended by extraction and construction of degree adverb dictionary, network word dictionary, negative word dictionary and other related dictionaries. Second, the sentiment value of a micro-blog text can be obtained through the calculation of the weight. Finally, micro-blog texts on a topic can be classified as positive, negative and neutral. Experimental results show the effectiveness of the proposed method.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 218 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
