Name
Title | ||
|---|---|---|
How did the discussion go: Discourse act classification in social media conversations. |
Abstract | ||
|---|---|---|
Over the last two decades, social media has emerged as almost an alternate world where people communicate with each other and express opinions about almost anything. This makes platforms like Facebook, Reddit, Twitter, Myspace, etc., a rich bank of heterogeneous data, primarily expressed via text but reflecting all textual and non-textual data that human interaction can produce. We propose a novel attention-based hierarchical LSTM model to classify discourse act sequences in social media conversations, aimed at mining data from online discussion using textual meanings beyond sentence level. The very uniqueness of the task is the complete categorization of possible pragmatic roles in informal textual discussions, contrary to extraction of question–answers, stance detection, or sarcasm identification which are very much role specific tasks. Early attempt was made on a Reddit discussion dataset. We train our model on the same data, and present test results on two different datasets, one from Reddit and one from Facebook. Our proposed model outperformed the previous one in terms of domain independence; without using platform-dependent structural features, our hierarchical LSTM with word relevance attention mechanism achieved F1-scores of 71% and 66%, respectively, to predict discourse roles of comments in Reddit and Facebook discussions. Efficiency of recurrent and convolutional architectures in order to learn discursive representation on the same task has been presented and analyzed, with different word and comment embedding schemes. Our attention mechanism enables us to inquire into relevance ordering of text segments according to their roles in discourse. We present a human annotator experiment to unveil important observations about modeling and data annotation. Equipped with our text-based discourse identification model, we inquire into how heterogeneous non-textual features like location, time, leaning of information, etc. play their roles in characterizing online discussions on Facebook. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-01872-6_6 | arXiv: Computation and Language |
Field | DocType | Volume |
Categorization,Sarcasm,Social media,Domain independence,Computer science,Human interaction,Natural language processing,Artificial intelligence,Data Annotation,Sentence,Online discussion | Journal | abs/1808.02290 |
Citations | PageRank | References |
0 | 0.34 | 20 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Subhabrata Dutta | 1 | 3 | 2.06 |
| Tanmoy Chakraborty | 2 | 466 | 76.71 |
| D. Das | 3 | 717 | 76.14 |