Parametric and Non-parametric User-aware Sentiment Topic Models

Yang, Zaihan; Kotov, Alexander; Mohan, Aravind; Lu, Shiyong

doi:10.1145/2766462.2767758

Cited by 36 publications

(15 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, it is noteworthy that scholars often tend to use a few strategies (Table 2) in conjunction, meaning that these are not mutually exclusive (e.g., Cao et al 2014;Sun et al 2013;Yang et al 2015).…”

Section: Topic Model Implementation and Extensionsmentioning

confidence: 99%

Topic modeling in marketing: recent advances and research opportunities

Reisenbichler

Reutterer

2018

J Bus Econ

View full text Add to dashboard Cite

Using a probabilistic approach for exploring latent patterns in high-dimensional co-occurrence data, topic models offer researchers a flexible and open framework for soft-clustering large data sets. In recent years, there has been a growing interest among marketing scholars and practitioners to adopt topic models in various marketing application domains. However, to this date, there is no comprehensive overview of this rapidly evolving field. By analyzing a set of 61 published papers along with conceptual contributions, we systematically review this highly heterogeneous area of research. In doing so, we characterize extant contributions employing topic models in marketing along the dimensions data structures and retrieval of input data, implementation and extensions of basic topic models, and model performance evaluation. Our findings confirm that there is considerable progress done in various marketing sub-areas. However, there is still scope for promising future research, in particular with respect to integrating multiple, dynamic data sources, including time-varying covariates and the combination of exploratory topic models with powerful predictive marketing models.

show abstract

Section: Topic Model Implementation and Extensionsmentioning

confidence: 99%

Topic modeling in marketing: recent advances and research opportunities

Reisenbichler

Reutterer

2018

J Bus Econ

View full text Add to dashboard Cite

show abstract

“…As mentioned in the introduction, social web communication styles vary on the social web in general (Burger, Henderson, Kim, & Zarrella, 2011;Mihalcea & Garimella, 2016;Volkova & Yoram, 2015), for expressing sentiment (Montero, Munezero, & Kakkonen, 2014;Thelwall, Wilkinson, & Uppal, 2010), and for evaluating products (Yang, Kotov, Mohan, & Lu, 2015). In many different (mainly offline) contests, males seem more inclined to discuss aspects of objects whereas females are more likely to refer to psychological and social issues (Newman, Groom, Handelman, & Pennebaker, 2008).…”

Section: Background: Algorithmic Biasmentioning

confidence: 99%

“…Analysts may implicitly assume that sentiment analysis results are unbiased because they are automatic but this is not necessarily true. Given the existence of clear gender differences in communication styles on the social web (Burger, Henderson, Kim, & Zarrella, 2011;Mihalcea & Garimella, 2016;Volkova & Yoram, 2015), including for expressing sentiment (Montero, Munezero, & Kakkonen, 2014;Thelwall, Wilkinson, & Uppal, 2010), interpreting sentiment (Guerini, Gatti, & Turchi, 2013) and discussing products (Yang, Kotov, Mohan, & Lu, 2015), gender biases in sentiment analysis seem likely. In other words, sentiment analysis algorithms may be more able to detect sentiment from one gender than from another so that, in a gender-mixed collection of texts, sentiment analysis results could over represent the opinions of one gender.…”

Section: Introductionmentioning

confidence: 99%

Gender bias in machine learning for sentiment analysis

Thelwall

2018

OIR

View full text Add to dashboard Cite

Purpose: This paper investigates whether machine learning induces gender biases in the sense of results that are more accurate for male authors than for female authors. It also investigates whether training separate male and female variants could improve the accuracy of machine learning for sentiment analysis. Design/methodology/approach: This article uses ratings-balanced sets of reviews of restaurants and hotels (3 sets) to train algorithms with and without gender selection. Findings: Accuracy is higher on female-authored reviews than on male-authored reviews for all data sets, so applications of sentiment analysis using mixed gender datasets will over represent the opinions of women. Training on same gender data improves performance less than having additional data from both genders. Practical implications:End users of sentiment analysis should be aware that its small gender biases can affect the conclusions drawn from it and apply correction factors when necessary. Users of systems that incorporate sentiment analysis should be aware that performance will vary by author gender. Developers do not need to create gender-specific algorithms unless they have more training data than their system can cope with. Originality/value: This is the first demonstration of gender bias in machine learning sentiment analysis.

show abstract

“…Several works in topic models utilize nontextual data such as prices (Iwata & Sawada, 2013), authors' demographic information (Yang, Kotov, Mohan, & Lu, 2015), and geographical locations (Kotov, Rakesh, Agichtein, & Reddy, 2015;Kotov, Wang, & Agichtein, 2013). Other works also proposed the inclusion of hashtags in topic models.…”

Section: Nontextual Data In Topic Modelsmentioning

confidence: 99%

Improving interpretations of topic modeling in microblogs

Alkhodair

Fung

Rahman

et al. 2017

Asso for Info Science & Tech

View full text Add to dashboard Cite

Topic models were proposed to detect the underlying semantic structure of large collections of text documents to facilitate the process of browsing and accessing documents with similar ideas and topics. Applying topic models to short text documents to extract meaningful topics is challenging. The problem becomes even more complicated when dealing with short and noisy micro‐posts in Twitter that are about one general topic. In such a case, the goal of applying topic models is to extract subtopics. This results in topics represented by similar sets of keywords, which in turn makes the process of topic interpretation more confusing. In this paper we propose a new method that incorporates Twitter‐LDA, WordNet, and hashtags to enhance the keyword labels that represent each topic. We emphasize the importance of different keywords to different topics based on the semantic relationships and the co‐occurrences of keywords in hashtags. We also propose a method to find the best number of topics to represent the text document collection. Experiments on two real‐life Twitter datasets on fashion suggest that our method performs better than the original Twitter‐LDA in terms of perplexity, topic coherence, and the quality of keywords for topic labeling.

show abstract

Parametric and Non-parametric User-aware Sentiment Topic Models

Cited by 36 publications

References 22 publications

Topic modeling in marketing: recent advances and research opportunities

Topic modeling in marketing: recent advances and research opportunities

Gender bias in machine learning for sentiment analysis

Improving interpretations of topic modeling in microblogs

Contact Info

Product

Resources

About