Charin Polpanumas

While coreference resolution is a well-established research area in Natural Language Processing (NLP), research focusing on Thai language remains limited due to the lack of large annotated corpora. In this work, we introduce ThaiCoref , a dataset for Thai coreference resolution. Our dataset comprises 777,271 tokens, 44,082 mentions and 10,429 entities across four text genres: university essays, newspapers, speeches, and Wikipedia. Our annotation scheme is built upon the OntoNotes benchmark with adjustments to address Thai-specific phenomena and cover more cases. Utilizing ThaiCoref, we train models employing a multilingual encoder and cross-lingual transfer techniques, achieving a best F1 score of 67.88% on the test set. Our error analysis reveals challenges posed by Thai’s unique morphological and syntactic features. To benefit the NLP community, we make the dataset and the model publicly available at http://www.github.com/nlp-chula/thai-coref.

In this paper, we study the online consumer review generation process by analyzing 37.12 million online reviews across nineteen product categories obtained from Amazon.com. This study revealed that the discrepancy between ratings by others and consumers’ post-purchasing evaluations significantly influenced both the valence and quantity of the reviews that consumers generated. Specifically, a negative discrepancy (‘worse than what I read’) significantly accelerates consumers to write negative reviews (19/19 categories supported), while a positive discrepancy (‘better than what I read’) accelerates consumers to write positive reviews (16/19 categories supported). This implies that others’ ratings play an important role in influencing the review generation process by consumers. More interestingly, we found that this discrepancy significantly influences consumers’ neutral review generation, which is known to amplify the effect of positive or negative reviews by affecting consumers’ search behavior or the credibility of the information. However, this effect is asymmetric. While negative discrepancies lead consumers to write more neutral reviews, positive discrepancies help reduce neutral review generation. Furthermore, our findings provide important implications for marketers who tend to generate fake reviews or selectively generate reviews favorable to their products to increase sales. Doing so may backfire on firms because negative discrepancies can accelerate the generation of objective or negative reviews.

This study drew on the existing decision process theory to empirically examine the effect of word of mouth (WOM) generated by social media. The prospect theory was adopted to illustrate how both the volume and valence of WOM influence a person9s decision to watch a movie through the movie quality evaluation stage. Using U.S. movie industry data and online post data from Twitter, the authors found that the effect of online WOM is well explained by the prospect theory. Particularly, findings suggest that intensively advertising a movie before its release to attract moviegoers could backfire by raising the moviegoers9 expectations.