Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Sentiment": models, code, and papers

Analyzing Roles of Classifiers and Code-Mixed factors for Sentiment Identification

Mar 15, 2018
Soumil Mandal, Dipankar Das

Multilingual speakers often switch between languages to express themselves on social communication platforms. Sometimes, the original script of the language is preserved, while using a common script for all the languages is quite popular as well due to convenience. On such occasions, multiple languages are being mixed with different rules of grammar, using the same script which makes it a challenging task for natural language processing even in case of accurate sentiment identification. In this paper, we report results of various experiments carried out on movie reviews dataset having this code-mixing property of two languages, English and Bengali, both typed in Roman script. We have tested various machine learning algorithms trained only on English features on our code-mixed data and have achieved the maximum accuracy of 59.00% using Naive Bayes (NB) model. We have also tested various models trained on code-mixed data, as well as English features and the highest accuracy of 72.50% was obtained by a Support Vector Machine (SVM) model. Finally, we have analyzed the misclassified snippets and have discussed the challenges needed to be resolved for better accuracy.

* 18th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2017 (RCS) 

  Access Paper or Ask Questions

Rule-Based Approach for Party-Based Sentiment Analysis in Legal Opinion Texts

Nov 13, 2020
Isanka Rajapaksha, Chanika Ruchini Mudalige, Dilini Karunarathna, Nisansa de Silva, Gathika Ratnayaka, Amal Shehan Perera

A document which elaborates opinions and arguments related to the previous court cases is known as a legal opinion text. Lawyers and legal officials have to spend considerable effort and time to obtain the required information manually from those documents when dealing with new legal cases. Hence, it provides much convenience to those individuals if there is a way to automate the process of extracting information from legal opinion texts. Party-based sentiment analysis will play a key role in the automation system by identifying opinion values with respect to each legal parties in legal texts.

* 2 pages, 1 figure, The 20th International Conference on Advances in ICT for Emerging Regions (ICTer2020) 

  Access Paper or Ask Questions

BERT for Sentiment Analysis: Pre-trained and Fine-Tuned Alternatives

Jan 10, 2022
Frederico Souza, João Filho

BERT has revolutionized the NLP field by enabling transfer learning with large language models that can capture complex textual patterns, reaching the state-of-the-art for an expressive number of NLP applications. For text classification tasks, BERT has already been extensively explored. However, aspects like how to better cope with the different embeddings provided by the BERT output layer and the usage of language-specific instead of multilingual models are not well studied in the literature, especially for the Brazilian Portuguese language. The purpose of this article is to conduct an extensive experimental study regarding different strategies for aggregating the features produced in the BERT output layer, with a focus on the sentiment analysis task. The experiments include BERT models trained with Brazilian Portuguese corpora and the multilingual version, contemplating multiple aggregation strategies and open-source datasets with predefined training, validation, and test partitions to facilitate the reproducibility of the results. BERT achieved the highest ROC-AUC values for the majority of cases as compared to TF-IDF. Nonetheless, TF-IDF represents a good trade-off between the predictive performance and computational cost.

* 10 pages, 1 figure, 3 tables. Accepted at International Conference on the Computational Processing of Portuguese (PROPOR 2022), but not yet published 

  Access Paper or Ask Questions

Understanding Pre-trained BERT for Aspect-based Sentiment Analysis

Oct 31, 2020
Hu Xu, Lei Shu, Philip S. Yu, Bing Liu

This paper analyzes the pre-trained hidden representations learned from reviews on BERT for tasks in aspect-based sentiment analysis (ABSA). Our work is motivated by the recent progress in BERT-based language models for ABSA. However, it is not clear how the general proxy task of (masked) language model trained on unlabeled corpus without annotations of aspects or opinions can provide important features for downstream tasks in ABSA. By leveraging the annotated datasets in ABSA, we investigate both the attentions and the learned representations of BERT pre-trained on reviews. We found that BERT uses very few self-attention heads to encode context words (such as prepositions or pronouns that indicating an aspect) and opinion words for an aspect. Most features in the representation of an aspect are dedicated to the fine-grained semantics of the domain (or product category) and the aspect itself, instead of carrying summarized opinions from its context. We hope this investigation can help future research in improving self-supervised learning, unsupervised learning and fine-tuning for ABSA. The pre-trained model and code can be found at https://github.com/howardhsu/BERT-for-RRC-ABSA.

* COLING 2020 

  Access Paper or Ask Questions

TweetCOVID: A System for Analyzing Public Sentiments and Discussions about COVID-19 via Twitter Activities

Mar 02, 2021
Jolin Shaynn-Ly Kwan, Kwan Hui Lim

The COVID-19 pandemic has created widespread health and economical impacts, affecting millions around the world. To better understand these impacts, we present the TweetCOVID system that offers the capability to understand the public reactions to the COVID-19 pandemic in terms of their sentiments, emotions, topics of interest and controversial discussions, over a range of time periods and locations, using public tweets. We also present three example use cases that illustrates the usefulness of our proposed TweetCOVID system.

* Accepted to the 26th International Conference on Intelligent User Interfaces (IUI'21), Demo Track 

  Access Paper or Ask Questions

ConTrip: Consensus Sentiment review Analysis and Platform ratings in a single score

Jan 06, 2022
José Bonet, José Bonet

People unequivocally employ reviews to decide on purchasing an item or an experience on the internet. In that regard, the growing significance and number of opinions have led to the development of methods to assess their sentiment content automatically. However, it is not straightforward for the models to create a consensus value that embodies the agreement of the different reviews and differentiates across equal ratings for an item. Based on the approach proposed by Nguyen et al. in 2020, we derive a novel consensus value named ConTrip that merges their consensus score and the overall rating of a platform for an item. ConTrip lies in the rating range values, which makes it more interpretable while maintaining the ability to differentiate across equally rated experiences. ConTrip is implemented and freely available under MIT license at https://github.com/pepebonet/contripscore

* 4 pagines, 1 figure 

  Access Paper or Ask Questions

Deep Sentiment Analysis using a Graph-based Text Representation

Feb 23, 2019
Kayvan Bijari, Hadi Zare, Hadi Veisi, Emad Kebriaei

Social media brings about new ways of communication among people and is influencing trading strategies in the market. The popularity of social networks produces a large collection of unstructured data such as text and image in a variety of disciplines like business and health. The main element of social media arises as text which provokes a set of challenges for traditional information retrieval and natural language processing tools. Informal language, spelling errors, abbreviations, and special characters are typical in social media posts. These features lead to a prohibitively large vocabulary size for text mining methods. Another problem with traditional social text mining techniques is that they fail to take semantic relations into account, which is essential in a domain of applications such as event detection, opinion mining, and news recommendation. This paper set out to employ a network-based viewpoint on text documents and investigate the usefulness of graph representation to exploit word relations and semantics of the textual data. Moreover, the proposed approach makes use of a random walker to extract deep features of a graph to facilitate the task of document classification. The experimental results indicate that the proposed approach defeats the earlier sentiment analysis methods based on several benchmark datasets, and it generalizes well on different datasets without dependency for pre-trained word embeddings.


  Access Paper or Ask Questions

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

Oct 01, 2021
Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis

Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization techniques based on few-shot learning, we propose UserIdentifier, a novel scheme for training a single shared model for all users. Our approach produces personalized responses by adding fixed, non-trainable user identifiers to the input data. We empirically demonstrate that this proposed method outperforms the prefix-tuning based state-of-the-art approach by up to 13%, on a suite of sentiment analysis datasets. We also show that, unlike prior work, this method needs neither any additional model parameters nor any extra rounds of few-shot fine-tuning.


  Access Paper or Ask Questions

Topic, Sentiment and Impact Analysis: COVID19 Information Seeking on Social Media

Aug 28, 2020
Md Abul Bashar, Richi Nayak, Thirunavukarasu Balasubramaniam

When people notice something unusual, they discuss it on social media. They leave traces of their emotions via text expressions. A systematic collection, analysis, and interpretation of social media data across time and space can give insights on local outbreaks, mental health, and social issues. Such timely insights can help in developing strategies and resources with an appropriate and efficient response. This study analysed a large Spatio-temporal tweet dataset of the Australian sphere related to COVID19. The methodology included a volume analysis, dynamic topic modelling, sentiment detection, and semantic brand score to obtain an insight on the COVID19 pandemic outbreak and public discussion in different states and cities of Australia over time. The obtained insights are compared with independently observed phenomena such as government reported instances.


  Access Paper or Ask Questions

ESAN: Efficient Sentiment Analysis Network of A-Shares Research Reports for Stock Price Prediction

Dec 03, 2021
Tuo Sun, Wanrong Zheng, Shufan Yu, Mengxun Li, Jiarui Ou

In this paper, we are going to develop a natural language processing model to help us to predict stocks in the long term. The whole network includes two modules. The first module is a natural language processing model which seeks out reliable factors from input reports. While the other is a time-series forecasting model which takes the factors as input and aims to predict stocks earnings yield. To indicate the efficiency of our model to combine the sentiment analysis module and the time-series forecasting module, we name our method ESAN.


  Access Paper or Ask Questions

<<
132
133
134
135
136
137
138
139
140
141
142
143
144
>>