Jiangwei Liu
Abstract
Chinese short text sentiment analysis can help understand society’s views on various hot topics. Many existing sentiment analysis methods are based on sentiment dictionaries. Still, sentiment dictionaries are easily affected by subjective factors. They require a lot of time to build as well as maintenance to prevent obsolescence. For the aim of extracting rich information within texts more effectively, we propose a Channel Attention TextCNN with Feature Word Extraction model (CAT-FWE). The feature word extraction module helps us choose words that affect the sentiment of reviews. Then, these words are integrated with multi-level semantic information to enhance the information of sentences. In addition, the channel attention textCNN module that is a promotion of traditional TextCNN tends to pay more attention to those meaningful features. It eliminates the impacts of features that do not make any sense effectively. We apply our CAT-FWE model to both fine-grained classification and binary classification tasks for Chinese short texts. Experiment results show that it can improve the performance of emotion recognition.
- [1] . 2020. Light gradient boosting machine for general sentiment classification on short texts: A comparative evaluation. IEEE Access 8 (2020), 101840–101858.Google Scholar
Cross Ref
- [2] . 2015. Deep residual learning for image recognition. CVPR (2016), 770--778. Retrieved from https://arxiv.org/abs/1512.03385.Google Scholar
- [3] . 2019. A survey on classification techniques for opinion mining and sentiment analysis. Artif. Intell. Rev. 52, 3 (2019), 1495–1545.Google ScholarDigital Library
- [4] . 2020. What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Comput. Educ. 145 (2020).Google ScholarDigital Library
- [5] . 1997. Long short-term memory. Neural Comput. 9, 8 (1997), 1735–1780.Google ScholarDigital Library
- [6] . 2018. A novel sentiment aware dictionary for multi-domain sentiment classification. Comput. Electr. Eng. 69 (2018), 585–597.Google ScholarCross Ref
- [7] . 2015. Microblog sentiment analysis with emoticon space model. J. Comput. Sci. Technol. 30, 5 (2015), 1120–1129.Google ScholarCross Ref
- [8] . 2017. Deep pyramid convolutional neural networks for text categorization. ACL 1 (2017), 562--570.Google Scholar
- [9] . 2014. Convolutional neural networks for sentence classification. EMNLP (2014), 1746--1751.Google Scholar
- [10] . 2020. Fine-grained emotion classification of Chinese microblogs based on graph convolution networks. World Wide Web 23, 5 (2020), 2771–2787.Google ScholarDigital Library
- [11] . 2013. Tracking sentiment in mail: How genders differ on emotional axes. WASSA@ACL (2011), 70--79.Google Scholar
- [12] . 2020. Application of the BERT language model for sentiment analysis of social network posts. RCAI (2020), 274--283.Google Scholar
- [13] . 2021. T-BERT - model for sentiment analysis of micro-blogs integrating topic model and BERT. CoRR abs/2106.01097.Google Scholar
- [14] . 2019. Gated recurrent neural network approach for multilabel emotion detection in microblogs. CoRR abs/1907.07653.Google Scholar
- [15] . 2018. Deep neural network architecture for sentiment analysis and emotion identification of Twitter messages. Multimedia Tools Appl. 77, 24 (2018), 32213–32242.Google ScholarDigital Library
- [16] . 2020. A novel LMAEB-CNN model for Chinese microblog sentiment analysis. J. Supercomput. 76, 11 (2020), 9127–9141.Google ScholarDigital Library
- [17] . 2021. Sentiment analysis of chinese short text based on multiple features. CONF-CDS (2021), 65:1--65:6.Google Scholar
- [18] . 2021. Chinese sentiment analysis based on lightweight character-level BERT. KST (2021), 27--32.Google Scholar
- [19] . 2017. Attention is all you need. NIPS (2017), 5998--6008.Google Scholar
- [20] . 2020. An optimal support vector machine based classification model for sentimental analysis of online product reviews. Fut. Gener. Comput. Syst. 111 (2020), 234–240.Google ScholarCross Ref
- [21] . 2014. Emotion classification in microblog texts using class sequential rules. AAAI (2014), 187--193.Google Scholar
- [22] . 2020. Sentiment lexicon for chinese college students to build and apply. CSAE (2020), 106:1--106:7.Google ScholarDigital Library
- [23] . 2019. Chinese micro-blog sentiment analysis based on multiple sentiment dictionaries and semantic rule sets. IEEE Access 7 (2019), 183924–183939.Google ScholarCross Ref
- [24] . 2020. Sentiment analysis for online reviews using conditional random fields and support vector machines. Electr. Commer. Res. 20, 2 (2020), 343–360.Google ScholarDigital Library
- [25] . 2019. Chinese text sentiment analysis based on extended sentiment dictionary. IEEE Access 7 (2019), 43749–43762.Google ScholarCross Ref
- [26] . 2020. Sentiment analysis for E-commerce product reviews in Chinese based on sentiment lexicon and deep learning. IEEE Access 8 (2020), 23522–23530.Google ScholarCross Ref
- [27] . 2020. Micro-blog sentiment classification method based on the personality and bagging algorithm. Fut. Internet 12, 4 (2020), 75.Google ScholarCross Ref
- [28] . 2012. Unsupervised feature learning framework for no- reference image quality assessment. CVPR (2012), 1098--1105.Google Scholar
- [29] . 2021. Sentiment classification model for Chinese micro-blog comments based on key sentences extraction. Soft Comput. 25, 1 (2021), 463–476.Google ScholarDigital Library
- [30] . 2018. Sentiment analysis of Chinese micro-blog text based on extended sentiment dictionary. Fut. Gener. Comput. Syst. 81 (2018), 395–403.Google ScholarDigital Library
- [31] . 2017. A sensitivity analysis of (and practitioners’ guide to) convolutional neural networks for sentence classification. IJCNLP 1 (2017), 253--263.Google Scholar
- [32] . 2016. Sentiment analysis: Mining opinions, sentiments, and emotions. Comput. Ling. 42, 3 (2016), 595–598.Google ScholarDigital Library
- [33] . 2019. Sentiment analysis of chinese microblog based on stacked bidirectional LSTM. IEEE Access 7 (2019), 38856–38866.Google ScholarCross Ref
Index Terms
- Channel Attention TextCNN with Feature Word Extraction for Chinese Sentiment Analysis
Recommendations
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Sentiment Analysis for Product Features in Chinese Reviews Based on Semantic Association
AICI '09: Proceedings of the 2009 International Conference on Artificial Intelligence and Computational Intelligence - Volume 03Often there are more than one topic, more than one holder and more than one sentiment in an opinion sentence. But currently the studies on sentiment analysis are unable to identify exactly which sentiments a holder expresses on a given topic, so these ...
Domain-specific sentiment analysis using contextual feature generation
TSA '09: Proceedings of the 1st international CIKM workshop on Topic-sentiment analysis for mass opinionThis paper presents a novel framework for sentiment analysis, which exploits sentiment topic information for generating context-driven features. Since the domain-specific nature of sentiment classification led the task more problematic, considering more ...
Comments