Journal ArticleDOI
Analysis of Sentiment on Movie Reviews Using Word Embedding Self-Attentive LSTM
S. Sivakumar,R. Rajalakshmi +1 more
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TLDR
A novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to Extract the key terms from the reviews.Abstract:
In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and relevant key terms from a sparse representation of the review is difficult. Word embedding improves the text classification by solving the problem of sparse matrix and semantics of the word. In this paper, a novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to extract the key terms from the reviews. Based on the experimental analysis on the IMDB dataset, the authors have shown that the proposed architecture word-embedding self-attention LSTM architecture achieved an F1 score of 88.67%, while LSTM and word embedding LSTM-based models resulted in an F1 score of 84.42% and 85.69%, respectively.read more
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Journal ArticleDOI
A Human-Robot Interaction System Calculating Visual Focus of Human’s Attention Level
Partha Chakraborty,Sabbir Ahmed,Mohammad Abu Yousuf,Akm Azad,Salem A. Alyami,Mohammad Ali Moni +5 more
TL;DR: In this paper, an Artificial Neural Network (ANN) and Recurrent Neural Network-Long Short Term Memory (LSTM) based deep learning (DL) architectures have been proposed for analysing the data.
Journal ArticleDOI
Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System
Sandipan Sahu,Raghvendra Kumar,Mohd. Shafi Pathan,Jana Shafi,Yogesh Kumar,Muhammad Fazal Ijaz +5 more
TL;DR: A content-based (CB) movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description, and a multiclass movie popularity prediction system, which outperforms all the benchmark models.
Journal ArticleDOI
Movie Popularity and Target Audience Prediction Using the Content-Based Recommender System
TL;DR: In this paper , a content-based movie recommendation system (RS) using preliminary movie features like genre, cast, director, keywords, and movie description was proposed to predict the movie popularity in the post-production stage.
Journal ArticleDOI
Modeling multi-prototype Chinese word representation learning for word similarity
TL;DR: A multi-prototype Chinese word representation model (MP-CWR) for word similarity based on synonym knowledge base, including knowledge representation module and word similarity module is proposed, which utilizes the synonyms as prior knowledge to supplement the relationship between words.
References
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Deep Learning-Based Document Modeling for Personality Detection from Text
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Journal ArticleDOI
Sentiment Analysis Is a Big Suitcase
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Latent semantic analysis for text-based research
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Journal ArticleDOI
Sentiment and Sarcasm Classification With Multitask Learning
TL;DR: This paper showed that knowledge in sarcasm detection can also be beneficial to sentiment classification, and presented a multitask learning-based framework using a deep neural network that models this correlation to improve the performance of both tasks in a multi-task learning setting.
Journal ArticleDOI
Machine Learning Based Intrusion Detection Systems for IoT Applications
Abhishek Verma,Virender Ranga +1 more
TL;DR: The main goals of this study are to motivate IoT security researchers for developing IDSs using ensemble learning, and suggesting appropriate methods for statistical assessment of classifier’s performance.