An attention-based deep learning method for text sentiment analysis

Social networks have changed the communication patterns significantly. Information available from different social networking sites can be well utilized for the analysis of users opinion. Hence, the organizations would benefit through the development of a platform, which can analyze public sentiments in the social media about their products and services to provide a value addition in their business process. Over the last few years, deep learning is very popular in the areas of image classification, speech recognition, etc. However, research on the use of deep learning method in sentiment analysis is limited. It has been observed that in some cases the existing machine learning methods for sentiment analysis fail to extract some implicit aspects and might not be very useful. Therefore, we propose a deep learning approach for aspect extraction from text and analysis of users sentiment corresponding to the aspect. A seven layer deep convolutional neural network (CNN) is used to tag each aspect in the opinionated sentences. We have combined deep learning approach with a set of rule-based approach to improve the performance of aspect extraction method as well as sentiment scoring method. We have also tried to improve the existing rule-based approach of aspect extraction by aspect categorization with a predefined set of aspect categories using clustering method and compared our proposed method with some of the state-of-the-art methods. It has been observed that the overall accuracy of our proposed method is 0.87 while that of the other state-of-the-art methods like modified rule-based method and CNN are 0.75 and 0.80 respectively. The overall accuracy of our proposed method shows an increment of 7–12% from that of the state-of-the-art methods.

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Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks

10.1007/978-981-13-7474-6 ◽

2019 ◽

Author(s):

Arindam Chaudhuri

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Learning Networks ◽

Text Sentiment Analysis

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LUX: Smart Mirror with Sentiment Analysis for Mental Comfort

Sensors ◽

10.3390/s21093092 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3092

Author(s):

Hyona Yu ◽

Jihyun Bae ◽

Jiyeon Choi ◽

Hyungseok Kim

Keyword(s):

Mental Health ◽

Deep Learning ◽

Natural Language Processing ◽

Sentiment Analysis ◽

Language Processing ◽

Korean Text ◽

News Reporting ◽

Language Structure ◽

Text Sentiment Analysis ◽

Deep Learning Model

As COVID-19 solidifies its presence in everyday life, the interest in mental health is growing, resulting in the necessity of sentiment analysis. A smart mirror is suitable for encouraging mental comfort due to its approachability and scalability as an in-home AI device. From the aspect of natural language processing (NLP), sentiment analysis for Korean lacks an emotion dataset regarding everyday conversation. Its significant differences from English in terms of language structure make implementation challenging. The proposed smart mirror LUX provides Korean text sentiment analysis with the deep learning model, which examines GRU, LSTM, CNN, Bi-LSTM, and Bi-GRU networks. There are four emotional labels: anger, sadness, neutral, and happiness. For each emotion, there are three possible interactive responses: reciting wise sayings, playing music, and sympathizing. The implemented smart mirror also includes more-typical functions, such as a wake-up prompt, a weather reporting function, a calendar, a news reporting function, and a clock.

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Weibo Text Sentiment Analysis Based on BERT and Deep Learning

Applied Sciences ◽

10.3390/app112210774 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10774

Author(s):

Hongchan Li ◽

Yu Ma ◽

Zishuai Ma ◽

Haodong Zhu

Keyword(s):

Deep Learning ◽

Public Opinion ◽

Sentiment Analysis ◽

Monitoring Network ◽

Feature Representation ◽

Vector Representation ◽

Text Data ◽

Proposed Model ◽

Sentiment Dictionary ◽

Text Sentiment Analysis

With the rapid increase of public opinion data, the technology of Weibo text sentiment analysis plays a more and more significant role in monitoring network public opinion. Due to the sparseness and high-dimensionality of text data and the complex semantics of natural language, sentiment analysis tasks face tremendous challenges. To solve the above problems, this paper proposes a new model based on BERT and deep learning for Weibo text sentiment analysis. Specifically, first using BERT to represent the text with dynamic word vectors and using the processed sentiment dictionary to enhance the sentiment features of the vectors; then adopting the BiLSTM to extract the contextual features of the text, the processed vector representation is weighted by the attention mechanism. After weighting, using the CNN to extract the important local sentiment features in the text, finally the processed sentiment feature representation is classified. A comparative experiment was conducted on the Weibo text dataset collected during the COVID-19 epidemic; the results showed that the performance of the proposed model was significantly improved compared with other similar models.

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