Sentiment Analysis and Vector Embedding: A Comparative Study

Jawale, Shila; Sawarkar, Sudhir

doi:10.1007/978-981-16-9967-2_30

Lecture Notes in Networks and Systems

2022

DOI: 10.1007/978-981-16-9967-2_30

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Sentiment Analysis and Vector Embedding: A Comparative Study

Shila Jawale

Sudhir Sawarkar

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Cited by 2 publications

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“…throughtheuseofexplainablemethodsortechniquessuchasvisualizationtechniquesandothers Jawale(2020),VanderMaaten(2008, Jawale(2023).…”

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Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

Jawale

Sawarker

2022

International Journal of Applied Evolutionary Computation

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Regarding the ubiquity of digitalization and electronic processing, an automated review processing system, also known as sentiment analysis, is crucial. There were many architectures and word embeddings employed for effective sentiment analysis. Deep learning is now-a-days becoming prominent for solving these problems as huge amounts of data get generated per second. In deep learning, word embedding acts as a feature representative and plays an important role. This paper proposed a novel deep learning architecture which represents hybrid embedding techniques that address polysemy, semantic and syntactic issues of a language model, along with justifying the model prediction. The model is evaluated on sentiment identification tasks, obtaining the result as F1-score 0.9254 and F1-score 0.88, for MR and Kindle dataset respectively. The proposed model outperforms many current techniques for both tasks in experiments, suggesting that combining context-free and context-dependent text representations potentially capture complementary features of word meaning. The model decisions justified with the help of visualization techniques such as t-SNE.

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“…throughtheuseofexplainablemethodsortechniquessuchasvisualizationtechniquesandothers Jawale(2020),VanderMaaten(2008, Jawale(2023).…”

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confidence: 99%

Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

Jawale

Sawarker

2022

International Journal of Applied Evolutionary Computation

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A Novel Deep Learning Language Model with Hybrid-GFX Embedding and Hyperband Search for Opinion Analysis

Jawale,

Sawarkar

2023

SN COMPUT. SCI.

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Sentiment Analysis and Vector Embedding: A Comparative Study

Cited by 2 publications

References 36 publications

Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

Amalgamation of Embeddings With Model Explainability for Sentiment Analysis

A Novel Deep Learning Language Model with Hybrid-GFX Embedding and Hyperband Search for Opinion Analysis

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