A hierarchical lstm model with multiple features for sentiment analysis of sina weibo texts

Shi, Shumin; Zhao, Meng; Guan, Jun; Li, Yaxuan; Huang, Heyan

doi:10.1109/ialp.2017.8300622

Cited by 20 publications

(15 citation statements)

References 7 publications

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“…The researchers obtained distinct set of vectors of feature supplied as input for LSTM layer using two separate pretrained vectors of words. A research from (Shi et al, 2017) retrieved the user interest and content based manually obtained characteristics of the given data obtained from social network site Sina Weibo in addition to the word embedding features. The results of the research are sent into an LSTM network for classification.…”

Section: Related Workmentioning

confidence: 99%

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

Ranjan

Daniel

2022

ADCAIJ

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The fast growth of Internet and social media has resulted in a significant quantity of texts based review that is posted on the platforms like social media. In the age of social media, analyzing the emotional context of comments using machine learning technology helps in understanding of QoS for any product or service. Analysis and classification of user’s review helps in improving the QoS (Quality of Services). Machine Learning techniques have evolved as a great tool for performing sentiment analysis of user’s. In contrast to traditional classification models. Bidirectional Long Short-Term Memory (BiLSTM) has obtained substantial outcomes and Convolution Neural Network (CNN) has shown promising outcomes in sentiment classification. CNN can successfully retrieve local information by utilizing convolutions and pooling layers. BiLSTM employs dual LSTM orientations for increasing the background knowledge accessible to deep learning based models. The hybrid model proposed here is to utilize the advantages of these two deep learning based models. Tweets of users for reviews of Indian Railway Services have been used as data source for analysis and classification. Keras Embedding technique is used as input source to the proposed hybrid model. The proposed model receives inputs and generates features with lower dimensions which generate a classification result. The performance of proposed hybrid model was compared using Keras and Word2Vec and observed effective improvement in the response of the proposed model with an accuracy of 95.19%.

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Section: Related Workmentioning

confidence: 99%

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

Ranjan

Daniel

2022

ADCAIJ

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“…They used two different pre-trained word vectors to obtain different feature vectors given as input to LSTM. Besides the word embedding features, a study [10] has extracted the user and content-based manual features of the dataset collected from the Chinese social media, which is known as Sina Weibo. The obtained features of their study serve as an input to the LSTM network for classification purposes.…”

Section: Literaturementioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Sentiment Classification

Salur¹,

Aydın²

2020

IEEE Access

147

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A massive use of social media platforms such as Twitter and Facebook by omnifarious organizations has increased the critical individual feedback on the situation, events, products, and services. However, sentiment classification plays an important role in the user's feedback evaluation. At present, deep learning such as long short-term memory (LSTM), gated recurrent unit (GRU), bidirectionally long short-term memory (BiLSTM) or convolutional neural network (CNN) are prevalently preferred in sentiment classification. Moreover, word embedding such as Word2Vec and FastText is closely examined in text for mapping closely related to the vectors of real numbers. However, both deep learning and word embedding methods have strengths and weaknesses. Combining the strengths of the deep learning models with that of word embedding is the key to high-performance sentiment classification in the field of natural language processing (NLP). In the present study, we propose a novel hybrid deep learning model that strategically combines different word embedding (Word2Vec, FastText, character-level embedding) with different deep learning methods (LSTM, GRU, BiLSTM, CNN). The proposed model extracts features of different deep learning methods of word embedding, combines these features and classifies texts in terms of sentiment. To verify the performance of the proposed model, several deep learning models called basic models were created to perform series of experiments. By comparing, the performance of the proposed model with that of past studies, the proposed model offers better sentiment classification performance. INDEX TERMS Sentiment classification, Turkish tweets analysis, hybrid model, word embedding, deep learning, LSTM, CNN.

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“…Some researchers [30], [31] have emphasized on the entity/topic of the tweet during classification. In the same manner, Shi et al [32] emphasize on the hierarchies by proposing hierarchical LSTM with textual and user profile features. Each deep learning method has its positive and negative aspects, therefore, researchers have proposed ensemble techniques with CNN, RNN, and LSTM to avoid the drawbacks of each model [33], [34].…”

Section: Deep Learning Sentiment Classificationmentioning

confidence: 99%

Context-Aware Sliding Window for Sentiment Classification

Masood

Abbasi

2020

IEEE Access

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Sentiment classification is an active area of research with applications in many domains. Many researchers in the past have proposed techniques to identify sentiments with reasonable accuracy. However, the focus is more on the syntactic and semantic features of the documents. These features are effective but they ignore the user's past sentiments. In this research, we hypothesize that the past sentiments help the classifier to effectively link the user's history along with the contents of the current tweet. Thus, allowing learning algorithms to correlate past activities in determining the current sentiments. For this sake, we propose three sliding window features to accumulate past sentiments from the time series data. In this paper, we propose seven variations of Context-aware Sliding Window (CSW) features on different machine learning and deep learning algorithms. Furthermore, we propose a temporal dataset of user tweets, which is manually labeled by nine human annotators. The proposed dataset consists of 36 users having 4,557 tweets. Results indicate significant improvements over six state-of-the-art baseline methods. INDEX TERMS Sentiment classification, user history, temporal sliding window, deep learning, machine-learning, temporal dataset.

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A hierarchical lstm model with multiple features for sentiment analysis of sina weibo texts

Cited by 20 publications

References 7 publications

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

Proposed Hybrid model for Sentiment Classification using CovNet-DualLSTM Techniques

A Novel Hybrid Deep Learning Model for Sentiment Classification

Context-Aware Sliding Window for Sentiment Classification

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