AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment Analysis

Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word selection technique to choose these seed categories and sentiment words. We explore unsupervised language model post-training to improve the overall performance, and propose a multi-label generator model to generate multiple aspect category-sentiment pairs per review sentence. Experiments conducted on four benchmark datasets showcase our method to outperform other weakly supervised baselines by a significant margin.Comment: to be published in EMNLP 202

Temporality as seen through translation: a case study on Hindi texts

Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which temporal orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified temporal orientation in English translated (both manual and automatic) sentences provides a good match with the temporal orientation of the Hindi texts. We also find that the task of manual temporal annotation becomes difficult in the translated texts while the automatic temporal processing system manages to correctly capture temporal information from the translations

Resolution of grammatical tense into actual time, and its application in Time Perspective study in the tweet space.

Time Perspective (TP) is an important area of research within the 'psychological time' paradigm. TP, or the manner in which individuals conduct themselves as a reflection of their cogitation of the past, the present, and the future, is considered as a basic facet of human functioning. These perceptions of time have an influence on our actions, perceptions, and emotions. Assessment of TP based on human language on Twitter opens up a new avenue for research on subjective view of time at a large scale. In order to assess TP of users' from their tweets, the foremost task is to resolve grammatical tense into the underlying temporal orientation of tweets as for many tweets the tense information, and their temporal orientations are not the same. In this article, we first resolve grammatical tense of users' tweets to identify their underlying temporal orientation: past, present, or future. We develop a minimally supervised classification framework for temporal orientation task that enables incorporating linguistic knowledge into a deep neural network. The temporal orientation model achieves an accuracy of 78.7% when tested on a manually annotated test set. This method performs better when compared to the state-of-the-art technique. Secondly, we apply the classification model to classify the users' tweets in either of the past, present or future categories. Tweets classified this way are then grouped for each user which gives rise to unidimensional TP. The valence (positive, negative, and neutral) is added to the temporal orientation dimension to produce the bidimensional TP. We finally investigate the association between the Twitter users' unidimensional and bidimensional TP and their age, education and six basic emotions in a large-scale empirical manner. Our analysis shows that people tend to think more about the past as well as more positive about the future when they age. We also observe that future-negative people are less joyful, more sad, more disgusted, and more angry while past-negative people have more fear

AC and DC insulation leakage errors in platinum resistance thermometers up to 1100\ub0C: Part 1: Factors influencing insulation leakage and its measurement

NRC publication: Ye

Fine-grained temporal orientation and its relationship with psycho-demographic correlates

Temporal orientation refers to an individual’s tendency to connect to the psychological concepts of past, present or future, and it affects personality, motivation, emotion, decision making and stress coping processes. The study of the social media users’ psychodemographic attributes from the perspective of human temporal orientation can be of utmost interest and importance to the business and administrative decision makers as it can provide an extra precious information for them to make informed decisions. In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets. We first create a temporal orientation classifier in a minimally supervised way which classifies each tweet of the users in one of the three temporal categories, namely past, present, and future. A deep Bi-directional Long Short Term Memory (BLSTM) is used for the tweet classification task. Our tweet classifier achieves an accuracy of 78.27% when tested on a manually created test set. We then determine the users’ overall temporal orientation based on their tweets on the social media. The sentiment is added to the tweets at the fine-grained level where each temporal tweet is given a sentiment with either of the positive, negative or neutral. Our experiment reveals that depending upon the sentiment view of temporal orientation, a user’s attributes vary. We finally measure the correlation between the users’ sentiment view of temporal orientation and their different psychodemographic factors using regression

Temporality as seen through translation: a case study on Hindi texts

Temporality has significantly contributed to various aspects of Natural Language Processing applications. In this paper, we determine the extent to which temporal orientation is preserved when a sentence is translated manually and automatically from the Hindi language to the English language. We show that the manually and automatically identified temporal orientation in English translated (both manual and automatic) sentences provides a good match with the temporal orientation of the Hindi texts. We also find that the task of manual temporal annotation becomes difficult in the translated texts while the automatic temporal processing system manages to correctly capture temporal information from the translations