Multimodal learning for topic sentiment analysis in microblogging

Huang, Faliang; Zhang, Shichao; Zhang, Jilian; Yu, Ge

doi:10.1016/j.neucom.2016.10.086

Cited by 75 publications

(46 citation statements)

References 13 publications

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“…Apart from mining the aspects and their sentiment, there exist also models which can extract emotions and personality traits from the comments (Bao et al, 2009;Rao et al, 2014aRao et al, , 2014bRao et al, , 2014cHuang et al, 2017). Customer personality and emotions determine how the customer react.…”

Section: Discussionmentioning

confidence: 99%

“…With the topic of emotion and personality detection is also related work of (Huang et al, 2017). Their multimodal joint sentiment topic model (MJST) inserts an additional sentiment layer into LDA and takes multimodal data such as emoticon or personality and text into consideration while inferring message sentiment.…”

Section: Modifications Of Lda Used In Emotion Miningmentioning

confidence: 99%

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Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

Sperkova¹

2018

AIP

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The aim of the article is to detect and review existing topic modelling methods of Latent Dirichlet Allocation and their modifications usable in Voice of Customer analysis. Voice of Customer is expressed mainly through textual comments which often focus on the evaluation of products or services the customer consumes. The most studied data source are customer reviews which contain next to the textual comments also ratings in form of scales. The aim of the topic models is to mine the topics and their aspects the customers are evaluating in their reviews and assign to them a particular sentiment or emotion. The author completed a systematic literature review of peer-reviewed published journal articles indexed in leading databases of Scopus and Web of Science and concerning the current use of Latent Dirichlet Allocation model variants in Voice of Customer textual analysis for performing the tasks of aspect detection, emotion detection, personality detection and sentiment assignation. In total, 38 modifications of the LDA model were identified with the reference to their first application in the research of text analytics. The review is intended for researchers in customer analytics the field of sentiment or emotion detection, and moreover as results from the review, for studies in personality recognition based on the textual data. The review offers a basic overview and comparison of LDA modifications which can be considered as a knowledge baseline for selection in a specific application. The scope of the literature examination is limited to the period of years 2003-2018 with the application relevant to the analysis of Voice of Customer subjective textual data only which is closely connected to the area of marketing or customer relationship management.

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Section: Discussionmentioning

confidence: 99%

Section: Modifications Of Lda Used In Emotion Miningmentioning

confidence: 99%

Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

Sperkova¹

2018

AIP

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“…ASUM works at the sentence level, and all the words of a sentence are generated under the same topic. Multimodal Joint Sentiment Topic (MJST) model (Huang, Zhang, Zhang, & Yu, ) attempts to improve the classification accuracy by adding emotional tag parameters. The only difference between JST and this model is that the presented model produces a word or emotional tag with a probability.…”

Section: Related Workmentioning

confidence: 99%

Enriched Latent Dirichlet Allocation for Sentiment Analysis

2020

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One of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other parameters. In this study, three novel topic models called date sentiment LDA (DSLDA), author–date sentiment LDA (ADSLDA), and pack–author–date sentiment LDA (PADSLDA) are proposed. The proposed models extend LDA through some extra parameters such as date, author, helpfulness, sentiment, and subtopic. The proposed models use helpfulness in the Gibbs sampling algorithm. Helpfulness is a part of readers who found the review helpful. The proposed models divide the words into two categories: the words more affected by the distribution of subtopic and the words more affected by the main topic. In this study, a new concept called pack is introduced, and a new model called PADSLDA is proposed for sentiment analysis at pack level. The proposed models outperformed the baseline models because according to evaluations results, the extra parameters can appropriately affect the generating process of words in a review. Sentiment analysis at the document level, perplexity, and topic coherence are the main parameters used in the evaluations.

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“…In addition, due to the continuous development of deep learning in various fields [34], the research of sentiment analysis gradually begins to tend to unsupervised classification [35][36][37][38]. Such methods are quite difficult and of great research significance also can save labor, but the method is not mature enough yet and the classified accuracy is relatively low which can't be used to the application now.…”

Section: Related Workmentioning

confidence: 99%

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

Chang¹,

Huo²

2018

NNW

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Text sentiment analysis plays an important role in social network information mining. It is also the theoretical foundation and basis of personalized recommendation, circle of interest classification and public opinion analysis. In view of the existing algorithms for feature extraction and weight calculation, we find that they fail to fully take into account the influence of sentiment words. Therefore, this paper proposed a fine-grained short text sentiment analysis method based on machine learning. To improve the calculation method of feature selection and weighting and proposed a more suitable sentiment analysis algorithm for features extraction named N-CHI and weight calculation named W-TF-IDF, increasing the proportion and weight of sentiment words in the feature words Through experimental analysis and comparison, the classification accuracy of this method is obviously improved compared with other methods.

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Multimodal learning for topic sentiment analysis in microblogging

Cited by 75 publications

References 13 publications

Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

Review of Latent Dirichlet Allocation Methods Usable in Voice of Customer Analysis

Enriched Latent Dirichlet Allocation for Sentiment Analysis

A Method of Fine-Grained Short Text Sentiment Analysis Based on Machine Learning

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