Query Reformulation Based on Word Embeddings: A Comparative Study

Security Informatics and Law Enforcement - Technology Development for Security Practitioners ◽

10.1007/978-3-030-69460-9_3 ◽

2021 ◽

pp. 41-55

Author(s):

Panos Panagiotou ◽

George Kalpakis ◽

Theodora Tsikrika ◽

Stefanos Vrochidis ◽

Ioannis Kompatsiaris

Keyword(s):

Comparative Study ◽

Query Reformulation ◽

Word Embeddings

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A Comparative Study on Word Embeddings in Deep Learning for Text Classification

Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval ◽

10.1145/3443279.3443304 ◽

2020 ◽

Author(s):

Congcong Wang ◽

Paul Nulty ◽

David Lillis

Keyword(s):

Deep Learning ◽

Comparative Study ◽

Text Classification ◽

Word Embeddings

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Word Embeddings for Semantic Resemblance of Substantial Text Data: A Comparative Study

Smart Systems and IoT: Innovations in Computing - Smart Innovation, Systems and Technologies ◽

10.1007/978-981-13-8406-6_30 ◽

2019 ◽

pp. 303-312

Author(s):

Kazi Lutful Kabir ◽

Fardina Fathmiul Alam ◽

Anika Binte Islam

Keyword(s):

Comparative Study ◽

Word Embeddings ◽

Text Data

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A Comparative Study of Word Embeddings for the Construction of a Social Media Expert Filter

10.1007/978-3-030-86967-0_15 ◽

2021 ◽

pp. 196-208

Author(s):

Jose A. Diaz-Garcia ◽

M. Dolores Ruiz ◽

Maria J. Martin-Bautista

Keyword(s):

Social Media ◽

Comparative Study ◽

Word Embeddings

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Comparative study of deep learning models for sentiment analysis

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.24459 ◽

2018 ◽

Vol 7 (2.14) ◽

pp. 5726

Author(s):

Oumaima Hourrane ◽

El Habib Benlahmar ◽

Ahmed Zellou

Keyword(s):

Deep Learning ◽

Comparative Study ◽

Sentiment Analysis ◽

Language Processing ◽

Word Embeddings ◽

Human Language ◽

Learning Models ◽

Automatic Learning ◽

Learning Capability ◽

The Web

Sentiment analysis is one of the new absorbing parts appeared in natural language processing with the emergence of community sites on the web. Taking advantage of the amount of information now available, research and industry have been seeking ways to automatically analyze the sentiments expressed in texts. The challenge for this task is the human language ambiguity, and also the lack of labeled data. In order to solve this issue, sentiment analysis and deep learning have been merged as deep learning models are effective due to their automatic learning capability. In this paper, we provide a comparative study on IMDB movie review dataset, we compare word embeddings and further deep learning models on sentiment analysis and give broad empirical outcomes for those keen on taking advantage of deep learning for sentiment analysis in real-world settings.

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