Text sentiment analysis methods

Previous articles:

Text sentiment analysis methods (1) - Lexicon-based sentiment analysis methods

Text sentiment analysis methods (2) - Traditional machine learning-based sentiment analysis methods

Text sentiment analysis methods (3) - Deep learning-based sentiment analysis methods

Conclusion

Through the introduction of the previous articles, we can predict that the use of deep learning for sentiment analysis is a future research trend in the field of natural language processing, where the scale of text data is expanding. From the development trend of different methods, future research on text sentiment analysis needs to focus on the following aspects:

1. By comparing different research methods, we can find that the existing research methods for sentiment analysis are mostly based on a single domain, such as social media Twitter, hotel reviews, etc.. In personalized recommendation, how to combine the content of multiple domains, perform sentiment classification, achieve better recommendation effect, and achieve in improving the generalization performance of the model are all worthy of future research and exploration.

2. Most of the research on sentiment analysis is mostly used for explicit text sentiment classification problems, using data sets containing obvious sentiment words, while the detection and classification of certain implicit words is not effective. At this stage, the research on implicit sentiment analysis is still in the initial stage and not very adequate. In the future, better sentiment classification can be achieved by building an implicit sentiment lexicon or by using better deep learning methods to extract semantic related information in a deeper way.

3. Research on sentiment analysis of complex utterances needs to be further improved. When online phrases with sentiment tendency appear more and more frequently, especially when the text contains ironic or metaphorical words, the detection of sentiment polarity will be difficult, which also needs further research.

4. Multimodal sentiment analysis is also a recent research hotspot. How to extract and fuse the sentiment information in multiple modalities is the main research direction. When the sentiment expressions in multiple modalities are inconsistent, how to weight the sentiment information in different modalities also needs to be considered; and whether external semantic information can be considered, and whether it is helpful to the accuracy of sentiment analysis, also needs to have a lot of research.

5. In the sub-task of sentiment analysis, it can also be found that most of the research is based on simple binary sentiment analysis, and achieving multi-categorization and more fine-grained sentiment analysis is also a hot topic for future research.

6. Pre-training model is a hot research topic at this stage. It can effectively solve the problems of traditional methods, such as the limitation of not being able to parallelize the computation, and can also effectively capture the interrelationship between words and achieve better results in downstream tasks by fine-tuning. However, it also suffers from the problem of large number of model parameters and long training time. How to achieve good classification results with a small number of model parameters and effectively shorten the training time would also be a direction worth studying.