Review based emotion profiles for cross domain recommendation

Chakraverty, Shampa; Saraswat, Mala

doi:10.1007/s11042-017-4767-x

Cited by 15 publications

(5 citation statements)

References 32 publications

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“…An emotion-based CDR scheme was explored using the movie and book domains as a case study. Experimental results on Movielens and Bookcrossing datasets indicated that the F1-measure score of proposed approach is 77.1% better than existing semantic clustering-based approach [41]. To summarize, in existing cross-media data analytics, studies have mainly focused on venue semantic modeling to bridge crossmedia understanding and retrieval [38]; the computer model of empirical sensing in cross-media settings to simulate synesthesia remains to be explored.…”

Section: Cross-media Affective Synesthesia Modelingmentioning

confidence: 99%

Image–Music Synesthesia-Aware Learning Based on Emotional Similarity Recognition

Xing

Zhang

et al. 2019

IEEE Access

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Synesthesia is a phenomenon in which human experience a cross-sensory interaction in perception. However, it is hard to bridge two sensory modalities in artificial intelligence. Emotion, the universal content across multiple media modalities, can be a cue to connect sensory perceptions for developing computer-based synesthetic intelligence. In this study, we present an image-music, cross-synesthesia-aware model based on their similarity in the emotion space. In this experiment, we built an affective synesthesia database of 250,000 image-music pairs. Multiple music and image features were extracted to form the database. Emotional representation is abstract and complex in perception, and the recognition of emotional similarity is fraught with uncertainty. In this work, Pearson correlation coefficient (PCC) and Euclidean distance (ED) method was compared to obtain the emotional similarity labels of each affective imagemusic pair. The proposed method could predict emotional similarity with mean squared error of 0.0075, demonstrating the effectiveness of our approach and may shed light on the development of cross-modal synesthesia-aware systems. INDEX TERMS Cross-modal retrieval, affective computing, synesthesia-aware modeling, Pearson correlation coefficient.

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Section: Cross-media Affective Synesthesia Modelingmentioning

confidence: 99%

Image–Music Synesthesia-Aware Learning Based on Emotional Similarity Recognition

Xing

Zhang

et al. 2019

IEEE Access

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“…The selection of an appropriate classification algorithm in sentiment analysis is must as its decision influences the behavior and end-product of the system. Chakraverty and Saraswat [14] have proposed Cross Domain Recommender (CDR) system which linkages source and target domains using users' emotional categories. These categories are-"love", "joy", "anger", "surprise", "sadness" and "fear".…”

Section: Related Workmentioning

confidence: 99%

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

Yadav¹,

Yadav

Jain

2018

ICST Transactions on Scalable Information Systems

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In today's society, sentiment analysis has gained due importance as it provides useful information about products that are used by variety of users. It gives a sneak peek of users' reactions towards the products that are available in the market at an early stage. It thus intimates users' perception and charts out a path that is beneficial for the market to grow as a whole. Although a lot of research is done to exploit the product based sentiment analysis but due to increase demand of the detailed components based products and their associated features, a novel method is desired to meet these criteria. So far, no such method is explored that analyses the product's components and their features simultaneously, on the basis of sentiments of the users. This paper proposes an improvised Feature Based Algorithm (FBA) for the sentiment analysis of product reviews while formulating a tree structure of product, components, and associated features. In addition, evaluation of double negative sentences, detecting questions and emotions from the review sentences are measured which increases efficiency of the FBA method. The comparison of product's components reviews is done with other existing algorithms-TF, TF-IDF and Naïve Bayes to demonstrate that the proposed FBA is coherent and auspicious.

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“…A new methodology for developing the links between source and target domain using user emotion profiles and items is presented [14]. The cross domain recommendation uses emotion lexicons to determine the emotions in the target domain.…”

Section: Dictionary Based Approachmentioning

confidence: 99%

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

Rani¹,

Sumathy²

2017

IJET

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Online social media and social networking services experience a drastic development in the present scenario. Contents generated by hundreds of millions of users are used for communication in general. Users mark their opinion and review in various applications such as Twitter, Facebook, YouTube, Weibo, Flicker, LinkedIn, Online-e commerce sites, Microblogging sites, etc. User generated text is spread rapidly on the web, and it has become tedious to analyze the opinionated text in order to arrive at a decision. Sentiment analysis, a subcategory of text mining is the major active research domain in current era due to greater quantity of opinionated text present in the Internet. Semantic detection is the sub-class in the sentiment analysis which is used for measuring the sentiment orientation in any text. Opinionated text is used for analyzing and making the decision simple. This interdisciplinary field draws various techniques from data mining, machine learning, natural language processing, lexicon based and hybrid based approaches. This paper provides a broad perspective with the highlight of the current state-of art techniques emphasizing the various research challenges and gaps present. The performance metrics in terms of detection rate, precision, recall, f-measure/score, average mean, auto-Pearson correlation, cosine similarity and ratio of time on various algorithms is discussed in detail. An analysis of the text mining approaches in different domains is presented.

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Review based emotion profiles for cross domain recommendation

Cited by 15 publications

References 32 publications

Image–Music Synesthesia-Aware Learning Based on Emotional Similarity Recognition

Image–Music Synesthesia-Aware Learning Based on Emotional Similarity Recognition

An Improvised Feature-Based Method for Sentiment Analysis of Product Reviews

Perspectives of the performance metrics in lexicon and hybrid based approaches: a review

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