Book ChapterDOI
Comparative Sentiment Analysis on a Set of Movie Reviews Using Deep Learning Approach
Koyel Chakraborty,Siddhartha Bhattacharyya,Rajib Bag,Aboul Ella Hassanien +3 more
- pp 311-318
TLDR
Google’s algorithm Word2Vec has been applied on a large movie review dataset to classify text so that the semantic associations between the terms stay conserved, and a comparative study of the performances of some notable clustering algorithms is demonstrated.Abstract:
This paper provides an insight to one of the recent additions in the turf of Machine Learning culture - the process of learning representation or features, known as Deep Learning It is highly anticipated that Deep Learning will fare much better than the traditional machine learning algorithms not only because of scalability but also of its ability to perform automatic feature extraction from raw data This paper deals with the analyzing of sentiments on a set of movie reviews, which is considered to be the most demanding facet of NLP’s In this paper, Google’s algorithm Word2Vec has been applied on a large movie review dataset to classify text so that the semantic associations between the terms stay conserved A comparative study of the performances of some notable clustering algorithms is demonstrated concerning their application involving a variable number of features and classifier types as well as variable number of clustersread more
Citations
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Journal ArticleDOI
A Survey of Sentiment Analysis from Social Media Data
TL;DR: The process of capturing data from social media over the years along with the similarity detection based on similar choices of the users in social networks are addressed.
Journal ArticleDOI
Comparative Study of Deep Learning-Based Sentiment Classification
TL;DR: Eight deep-learning models, three based on convolutional neural networks and five based on recurrent neural networks, with two types of input structures, i.e., word level and character level, are compared for 13 review datasets and the classification performances are discussed under different perspectives.
Journal ArticleDOI
Multimodal sentimental analysis for social media applications: A comprehensive review
TL;DR: This work aims to present a survey of recent developments in analyzing the multimodal sentiments (involving text, audio, and video/image) which involve human–machine interaction and challenges involved in analyzing them.
Journal ArticleDOI
Unsupervised intrusion detection through skip-gram models of network behavior
TL;DR: Skip-gram modeling, a word2vec algorithm variant, was leveraged to model systems’ legitimate network behavior and the resulting model was used to spot intrusions in a test dataset, leading to 99.20% precision, 82.07% recall, and 91.02% accuracy.
Journal ArticleDOI
Social Network and Sentiment Analysis: Investigation of Students’ Perspectives on Lecture Recording
TL;DR: The authors analyzed transcripts of discussions on social media (Facebook) that students generated on the value of lecture recordings and found that students generally view lecture recordings as resources for supplementing live lectures rather than replacing them.
References
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Book
Learning Deep Architectures for AI
TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
Proceedings ArticleDOI
Mining and summarizing customer reviews
Minqing Hu,Bing Liu +1 more
TL;DR: This research aims to mine and to summarize all the customer reviews of a product, and proposes several novel techniques to perform these tasks.
Posted Content
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: A simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (Thumbs down) if the average semantic orientation of its phrases is positive.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
Proceedings Article
Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews
TL;DR: This article proposed an unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended(thumbs down) based on the average semantic orientation of phrases in the review that contain adjectives or adverbs.