LDPP at the F in NLP -2022 ERAI Task: Determinantal Point Processes and Variational Auto-encoders for Identifying High-Quality Opinions from a pool of Social Media Posts (2022)

Abstract: Social media and online forums have made it easier for people to share their views and opinions on various topics in society. In this paper, we focus on posts discussing investment related topics. When it comes to investment , people can now easily share their opinions about online traded items and also provide rationales to support their arguments on social media. However, there are millions of posts to read with potential of having some posts from amateur investors or completely unrelated posts. Identifying the most important posts that could lead to higher maximal potential profit (MPP) and lower maximal loss for investment is not a trivial task. In this paper, propose to use determinantal point processes and variational autoencoders to identify high quality posts from the given rationales. Experimental results suggest that our method mines quality posts compared to random selection and also latent variable modeling improves improves the quality of selected posts.

## introduction the internet revolution and the social media era has made it easy for the public to create and share information including their opinions about certain aspects in society like politics @xcite , economy @xcite , finance @xcite and investment @xcite . when it comes to investment, it is now so simple for people to share their opinions about online traded items in online platforms, stock investment websites in real time. these numerous public comments are of great value in reflecting market conditions and making trading decisions. the open nature of most of these online platforms means that anyone can share any information whether they are experts on the topic being discussed or not. this presents a serious challenge in identifying high quality opinions especially for critical purposes like investment from such a large crowd of mined results. when people are giving their investments opinions, they provide supporting augments which we define as rationales supporting their reasoning. in the paper, we use the rationales behind the view points by sorting out the opinions that would to higher maximal potential profit (mpp) and lower maximal loss (ml) @xcite . the majority of the previous studies have lied on the idea of large numbers using average results obtained from popular tasks for example opinion mining and sentiment analysis. the most recent and competitive approach by @xcite identifies posts of high quality by making an assumption that these posts will have similar characteristics as those written by experts. in this work, we present an approach based on idea that high quality opinions are those that are less redundant but at the same time highly valuable. unlike the previous approaches, we do not use any documents written by experts since they may not be available but rather only base on contextualized representations from the provided rationales using sentence transformers @xcite . we select begin by identifying groups of similar opinions by performing joint dimensionality reduction and deep clustering on the embedding space of the opinions using variational autoencoders @xcite . determinantal point process @xcite are then used to select a set of representative opinions from the groups identified by variational autoencoders while maintaining high diversity among them. ## related work social media and online data have exponentially grown in the last few years and many domains are trying it to leverage to their advantage. some previous works have focused on utilizing user-generated data from social media @xcite , online forums @xcite , and e-commerce platforms @xcite . most existing works aims to find clusters, topics, classes or categories from social media data @xcite . these approaches usually take into account the law of large numbers and simply average the results extracted from tasks such as opinion mining and sentiment analysis. few of the previous works have focused on evaluating opinion quality. zhongyu and yang used feature-based methods using textual information in the comments and social interaction related features @xcite . ying and duboue provided an annotated pilot dataset and used a vanilla neural network with semantic information to classification @xcite . the most recent work is then one by @xcite ) that leverages high-accuracy models trained on documents written by experts and the crowd to mine high quality opinions from the crowd. in contract, to the existing work, we take a purely unsupervised approach using determinantal point processes and variational autoencoders without assuming access to documents written by experts. ## methodology this section describes our proposed methodology to identify the most import important opinions from a pool of opinions provided by amateur investors. our methodology is based on the assumption that the most important articles should not be redundant but at the same time should contain the most valuable information that could lead to higher mpp and lower ml. ## conclusion in this paper, we propose an approach that identifies the most diverse and important opinions a large pool of opinions as high quality opinions. the proposed approach approach (dpp-vae) combines variational auto-encoders and determinantal point process (dpp) to mine top quality opinions. the rationale behind our methods is that looking at diverse and well-represented opinions from a large crowd is more likely to lead to average maximal potential profit (mpp) and lower maximal loss (ml). experimental results reveal that our proposed method improves over baseline determinan-tal point process (dpp) and also achieves significant performance gains over random selection. results further reveal that the gain obtained from our method (dpp-vae) on average ml is much more than that obtained on average mpp. we attribute this to the fact that reading diverse opinions from different investors may be seen as a more risk averse strategy. as future work, it is important to experiment how guiding a determinantal point process selection with a few opinions written by experts would boost its performance and also extending the methodology beyond the erai dataset.