A multi domains short message sentiment classification using hybrid neural network architecture (original) (raw)
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Sentiment analysis is the computational study of opinions and emotions expressed in text. Deep learning is a model that is currently producing stateof-the-art in various application domains, including sentiment analysis. Many researchers are using a hybrid approach that combines different deep learning models and has been shown to improve model performance. In sentiment analysis, input in text data is first converted into a numerical representation. The standard method used to obtain a text representation is the fine-tuned embedding method. However, this method does not pay attention to each word's context in the sentence. Therefore, the Bidirectional Encoder Representation from Transformer (BERT) model is used to obtain text representations based on the context and position of words in sentences. This research extends the previous hybrid deep learning using BERT representation for Indonesian sentiment analysis. Our simulation shows that the BERT representation improves the accuracies of all hybrid architectures. The BERT-based LSTM-CNN also reaches slightly better accuracies than other BERT-based hybrid architectures.
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The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.
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2019 6th International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), 2019
The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, Bi-LSTM and CNN were established to judge the tendency of the user's implicit sentiment text. Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The Bi-LSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.
24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS, 2022
Abstract. Nowadays social media plays a significant role in all sorts of our activities ranging from analysing the attitude of a person for the job, getting opinions towards buying a product, acting as a forum for exchanging thoughts about the current events of various domains, creating awareness to the public about the natural calamities, educating the public about the fraudulent news spread by the fakers, initiating the young aspirant to protest against any societal issues, etc. Grasping the opinions shared by the experienced people towards a product, film, event, news, or politics like any subject of matter is one among the worth noting applications for a common man. It extends its application to making decisions about our day-to-day activities. The text reviews consist of enormous, sparse, non-uniform distribution of words represented as features. Text mining is the backend process for those applications. It includes techniques such as feature representation, sentiment classification, feature optimization, etc. Analysing the opinions suggested by the experienced people as positive and negative reviews is a challenging process and it is the baseline of our work. This paper contributes to the related processes involved in analysing the sentiments from the text reviews and accurately classifying them based on their polarity. In the proposed work, we particularly focus on feature representation techniques that have a major effect on enhancing the performance of sentiment classification. We explore different feature representation models such as TF-IDF vectorizer, word2vec vectorizer, and glove vectorizer as these word embedding models are interpreting the words and their syntactic and semantic relationships differently from the corpus. Also, we employ machine learning algorithms and a deep convolution neural network to perform comparative studies in classifying the sentiments. The word2vec in combination with Deep Convolution Neural Network provides the accuracy of 85.7%, precision of 84.4%, recall of 87%, and F- measure of 85.7% compared to other models.
International Journal of Advanced Research in Engineering and Technology (IJARET), 2020
Sentiment analysis has become the most popular research topic due to its various application in business, politics, entertainment, however analyzing opinion of people from short text such as Twitter message and single sentence is quite a challenging task due to their informality, misspell and semantic error. In this study, we propose character level multiscale sentiment analysis for Afaan Oromoo using combined Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) approach. Since there is no standardized and sufficient corpus prepared for Afaan Oromoo Natural Language Processing (NLP) task including sentiment analysis so far, we have collected data from two domain, Facebook and Twitter for the experiment. After collecting data, we removed user names, links, none Afaan Oromoo texts, and any unnecessary characters. The cleaned data were annotated manually by 4 different annotators into five class namely, 2 ,1,-2,-1, and 0 which represent very positive, positive, very negative, negative and neutral respectively. This multi-scale sentiment analysis provides a more refined analysis, which is vital for prioritizing and comparison of different opinion. Afterward we performed experiments on the prepared corpus from Facebook and and Twitter dataset. Based on the implemented Facebook dataset we achieved a promising performance accuracy of 93.3%, 91.4%, and 94.1% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. Consequently, we executed twitter dataset and achieved 92.6%, 90.3%, 93.8% for CNN, Bi-LSTM and CNN-Bi-LSTM respectively. The result suggests the possibility of multi-scale sentiment analysis as well as CNN-Bi-LSTM on Afaan Oromoo. We have also suggested that the accuracy can be improved by building standardized and sufficient amount of data set, which was one of the most difficult and demanding tasks of our work