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Joint Author Sentiment Topic Model
 

Joint Author Sentiment Topic Model

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Joint Author Sentiment Topic Model, Subhabrata Mukherjee, Gaurab Basu and Sachindra Joshi, In Proc. of the SIAM International Conference in Data Mining (SDM 2014), Pennsylvania, USA, Apr 24-26, 2014 ...

Joint Author Sentiment Topic Model, Subhabrata Mukherjee, Gaurab Basu and Sachindra Joshi, In Proc. of the SIAM International Conference in Data Mining (SDM 2014), Pennsylvania, USA, Apr 24-26, 2014 [http://people.mpi-inf.mpg.de/~smukherjee/jast.pdf]

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    Joint Author Sentiment Topic Model Joint Author Sentiment Topic Model Presentation Transcript

    • Joint Author Sentiment Topic Model Subhabrata Mukherjee Max Planck Institute for Informatics Gaurab Basu and Sachindra Joshi IBM India Research Lab April 25, 2014
    • April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”        Aspect Rating and Review Rating
    • April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration      Aspect Rating and Review Rating
    • April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.     Aspect Rating and Review Rating
    • April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.  Overall review rating - aggregation of facet-specific sentiments    Aspect Rating and Review Rating
    • April 25, 2014 “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Identify topics - direction, story and acting  Story has facets - plot and narration  Identify facet sentiments – great (plot), powerful (story), sloppy (acting) etc.  Overall review rating - aggregation of facet-specific sentiments  Why joint modeling ?  Sentiment words help locating topic words and vice-versa  Neighboring words establish semantics / sentiment of terms Aspect Rating and Review Rating
    • Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”          April 25, 2014
    • Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting       April 25, 2014
    • Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?     April 25, 2014
    • Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?  Author-writing style helps in locating / associating facets and sentiments  E.g. topic switch, verbosity, use of content and function words etc.  The author makes a topic switch in above review using the function word however  April 25, 2014
    • Why Author-Specificity ? “ [ This film is based on a true-life incident. It sounds like a great plot and the director makes a decent attempt in narrating a powerful story. ] [ However, the film does not quite make the mark due to sloppy acting. ] ”  Overall rating varies for authors with different topic preferences  Positive for those with greater preference for acting and narration  Negative for acting  Affective sentiment value varies for authors  How much negative is “does not quite make the mark” for me ?  Author-writing style helps in locating / associating facets and sentiments  E.g. topic switch, verbosity, use of content and function words etc.  The author makes a topic switch in above review using the function word however  Traditional works learn a global model independent of the review author April 25, 2014
    • Why care about writing style or coherence?  Better association of facets to topics by detecting semantic-syntactic class transitions and topic switch  semantic dependencies - association between facets to topics  syntactic dependencies - connection between facets and background words required to make the review coherent and grammatically correct April 25, 2014
    • Contributions  Show that author identity helps in rating prediction      April 25, 2014
    • Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences    April 25, 2014
    • Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style   April 25, 2014
    • Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style writing style  April 25, 2014
    • Contributions  Show that author identity helps in rating prediction  Author-specific generative model of a review that incorporates author-specific topic and facet preferences grading style writing style maintain coherence in reviews April 25, 2014
    • Topic Models April 25, 2014
    • Topic Models April 25, 2014 1. LDA Model
    • Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model
    • Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model 3. Joint Sentiment Topic Model
    • Topic Models April 25, 2014 1. LDA Model 2. Author-Topic Model 3. Joint Sentiment Topic Model 4. Topic Syntax Model
    • Generative Process for a Review April 25, 2014 Visit Restaurant
    • Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4
    • Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topics to write on I will write about food, ambience and …
    • Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and …
    • Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and … Topic Opinion It makes awesome Pasta. But the ambience is loud.
    • Generative Process for a Review April 25, 2014 Visit Restaurant Overall Impression …I think I will give overall rating +4 Topic Ratings I will give food +5 . It makes awesome Pasta … my favorite !!! But the ambience is loud… I will give it +2. But I do not care about it much Topics to write on I will write about food, ambience and … Topic Opinion It makes awesome Pasta. But the ambience is loud. How to write it ?
    • Generative Process for a Review April 25, 2014 How to write it ?
    • Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ?
    • Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ?
    • Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ? Topic Label ?
    • Generative Process for a Review April 25, 2014 How to write it ? Topic Word ? Background Word ? New Topic ? Current Topic ? Topic Label ? Word
    • JAST Model
    • JAST Model 1. For each document d, author a chooses overall rating r ~ Multinomial(Ω) from author-specific overall document rating distribution
    • JAST Model 2. For each topic z and each sentiment label l, draw ξz, l ~ Dirichlet(γ) 3. For each class c and each sentiment label l = 0, draw ξc, l ~ Dirichlet(δ)
    • JAST Model 4. Choose author-specific class transition distribution π Author Writing Style
    • JAST Model 5. Author a chooses author-rating specific topic-label distribution ϕa, r ~ Dirichlet(α) Author-Topic Preference Author Emotional Attachment to Topics Author Grading Style
    • JAST Model5. For each word w in the document
    • JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l).
    • JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l).
    • JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). Semantic Dependencies and Review Coherence
    • JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). Review Coherence and Syntactic Dependencies
    • JAST Model5. For each word w in the document b. If c = 1, Draw z, l ~ Multinomial(ϕa,r) . Draw w ~ Multinomial(ξz,l). d. If c≠ 1, 2, Draw w ~ Multinomial(ξc,l). Review Coherence and Syntactic Dependencies
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing  We use collapsed Gibb's sampling for estimating the parameters  Conditional distribution for joint updation of the latent variables is given by : April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Inferencing April 25, 2014
    • Dataset for Evaluation  IMDB movie review dataset  TripAdvisor restaurant review dataset April 25, 2014
    • Baselines  Lexical classification using majority voting  Joint Sentiment Topic Model1  Author-Topic LR Model2  Model Prior A sentiment lexicon is used to initialize the prior polarity of words in ξT x L[w] April 25, 2014 1. Chenghua Lin and Yulan He, Joint sentiment/topic model for sentiment analysis, CIKM '09, pp. 375-384. 2. Subhabrata Mukherjee, Gaurab Basu, and Sachindra Joshi, Incorporating author preference in sentiment rating prediction of reviews, WWW 2013.
    • Model Initialization Parameters April 25, 2014
    • Model Initialization Parameters April 25, 2014
    • Model Initialization Parameters April 25, 2014 Minimize Model Perplexity
    • Model Comparison with Baselines April 25, 2014
    • Model Comparison with Baselines April 25, 2014 IMDB Movie Review Dataset
    • Model Comparison with Baselines April 25, 2014 IMDB Movie Review Dataset TripAdvisor Restaurant Review Dataset
    • April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset
    • April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset
    • April 25, 2014 ComparisonwithTopPerformingModelsinIMDBDataset
    • Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014
    • Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014
    • Snapshot of Topic-Label-Word Extraction by JAST April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - TripAdvisor April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014
    • Snapshot of Author-Rating-Topic-Label Distribution Extracted by JAST - IMDB April 25, 2014
    • Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known      April 25, 2014
    • Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch    April 25, 2014
    • Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch  JAST is unsupervised, with overhead of knowing author identity  Performs better than all unsupervised/semi-supervised models and some supervised models  April 25, 2014
    • Conclusions  Sentiment classification and aspect rating prediction models can be improved if author is known  Authorship information helps in identifying author topic preferences, and author writing style to maintain review coherence  Semantic-syntactic class transition and topic switch  JAST is unsupervised, with overhead of knowing author identity  Performs better than all unsupervised/semi-supervised models and some supervised models  It will be interesting to use JAST for authorship attribution task April 25, 2014
    • QUESTIONS ??? April 25, 2014