A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
Yang, Min and Zhu, Dingju and Chow, Kam-Pui

Article Structure

Abstract

Emotion lexicons play a crucial role in sentiment analysis and opinion mining.

Introduction

Due to the popularity of opinion-rich resources (e.g., online review sites, forums, blogs and the microblogging websites), automatic extraction of opinions, emotions and sentiments in text is of great significance to obtain useful information for social and security studies.

Related Work

Emotion lexicon plays an important role in opinion mining and sentiment analysis.

Algorithm

In this section, we rigorously define the emotion-aware LDA model and its learning algorithm.

Experiments

In this section, we report empirical evaluations of our proposed model.

Conclusions and Future Work

In this paper, we have presented a novel emotion-aware LDA model that is able to quickly build a fine-grained domain-specific emotion lexicon for languages without many manually constructed resources.

Topics

LDA

Appears in 8 sentences as: LDA (8)
In A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
  1. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domain-specific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise.
    Page 1, “Abstract”
  2. The proposed EaLDA model extends the standard Latent Dirichlet Allocation ( LDA ) (Blei et al., 2003) model by employing a small set of seeds to guide the model generating topics.
    Page 1, “Introduction”
  3. Our approach relates most closely to the method proposed by Xie and Li (2012) for the construction of lexicon annotated for polarity based on LDA model.
    Page 2, “Related Work”
  4. In this section, we rigorously define the emotion-aware LDA model and its learning algorithm.
    Page 2, “Algorithm”
  5. Like the standard LDA model, EaLDA is a generative model.
    Page 2, “Algorithm”
  6. The generative process of word distributions for non-emotion topics follows the standard LDA definition with a scalar hyperparameter 607’).
    Page 2, “Algorithm”
  7. In this paper, we have presented a novel emotion-aware LDA model that is able to quickly build a fine-grained domain-specific emotion lexicon for languages without many manually constructed resources.
    Page 5, “Conclusions and Future Work”
  8. The proposed EaLDA model extends the standard LDA model by accepting a set of domain-independent emotion words as prior knowledge, and guiding to group semantically related words into the same emotion category.
    Page 5, “Conclusions and Future Work”

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fine-grained

Appears in 7 sentences as: fine-grained (7)
In A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
  1. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domain-specific lexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain.
    Page 1, “Abstract”
  2. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon.
    Page 1, “Abstract”
  3. As the fine-grained annotated data are expensive to get, the unsupervised approaches are preferred and more used in reality.
    Page 1, “Introduction”
  4. Usually, a high quality emotion lexicon play a significant role when apply the unsupervised approaches for fine-grained emotion classification.
    Page 1, “Introduction”
  5. The results demonstrate that our EaLDA model improves the quality and the coverage of state-of-the-art fine-grained lexicon.
    Page 2, “Introduction”
  6. The experimental results show that our algorithm can successfully construct a fine-grained domain-specific emotion lexicon for this corpus that is able to understand the connotation of the words that may not be obvious without the context.
    Page 4, “Experiments”
  7. In this paper, we have presented a novel emotion-aware LDA model that is able to quickly build a fine-grained domain-specific emotion lexicon for languages without many manually constructed resources.
    Page 5, “Conclusions and Future Work”

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hyperparameters

Appears in 6 sentences as: hyperparameter (1) hyperparameters (5)
In A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
  1. scalars 756) and vie) are hyperparameters of the
    Page 2, “Algorithm”
  2. The generative process of word distributions for non-emotion topics follows the standard LDA definition with a scalar hyperparameter 607’).
    Page 2, “Algorithm”
  3. They are generated from Dirichlet priors Dir(oz(e)) and Dir(a(n)) with 04(5) and 0407’) being hyperparameters .
    Page 2, “Algorithm”
  4. Assuming hyperparameters 04, 04(6), 0d”), and 6(6), 607’) , we develop a collapsed Gibbs sampling algorithm to estimate the latent variables in the EaLDA model.
    Page 3, “Algorithm”
  5. We first settle down the implementation details for the EaLDA model, specifying the hyperparameters that we choose for the experiment.
    Page 4, “Experiments”
  6. We set topic number M = 6, K = 4, and hyperparameters 04 = 0.75, 04(6) 2 ox”) = 045,001) = 0.5.
    Page 4, “Experiments”

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classification task

Appears in 4 sentences as: classification task (4)
In A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
  1. Since there is no metric explicitly measuring the quality of an emotion lexicon, we demonstrate the performance of our algorithm in two ways: (1) we perform a case study for the lexicon generated by our algorithm, and (2) we compare the results of solving emotion classification task using our lexicon against different methods, and demonstrate the advantage of our lexicon over other lexicons and other emotion classification systems.
    Page 4, “Experiments”
  2. We compare the performance between a popular emotion lexicon WordNet-Affect (Strapparava and Valitutti, 2004) and our approach for emotion classification task .
    Page 5, “Experiments”
  3. In particular, we are able to obtain an overall Fl-score of 10.52% for disgust classification task which is difficult to work out using pre-
    Page 5, “Experiments”
  4. Experimental results showed that the emotional lexicons generated by our algorithm is of high quality, and can assist emotion classification task .
    Page 5, “Conclusions and Future Work”

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generative process

Appears in 3 sentences as: generative process (3)
In A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon
  1. The generative process of word distributions for non-emotion topics follows the standard LDA definition with a scalar hyperparameter 607’).
    Page 2, “Algorithm”
  2. We summarize the generative process of the EaLDA model as below:
    Page 3, “Algorithm”
  3. As an alternative representation, the graphical model of the the generative process is shown by Figure 1.
    Page 3, “Algorithm”

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