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"Sentiment Analysis": models, code, and papers

Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions

Aug 30, 2019
Arezoo Hatefi Ghahfarrokhi, Mehrnoush Shamsfard

In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon-based and learning-based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in TSE is different depending on their attributes. Moreover, we indicate that for predicting the closing price only comments volume and for predicting the daily return both the volume and the sentiment of the comments could be useful. We demonstrate that Users' Trust coefficients have different behaviors toward the three stocks.

* submitted to "Intelligent Systems in Accounting, Finance, and Management" journal 
  

[email protected]: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text

Jan 22, 2021
Suman Dowlagar, Radhika Mamidi

Code-mixing(CM) is a frequently observed phenomenon that uses multiple languages in an utterance or sentence. CM is mostly practiced on various social media platforms and in informal conversations. Sentiment analysis (SA) is a fundamental step in NLP and is well studied in the monolingual text. Code-mixing adds a challenge to sentiment analysis due to its non-standard representations. This paper proposes a meta embedding with a transformer method for sentiment analysis on the Dravidian code-mixed dataset. In our method, we used meta embeddings to capture rich text representations. We used the proposed method for the Task: "Sentiment Analysis for Dravidian Languages in Code-Mixed Text", and it achieved an F1 score of $0.58$ and $0.66$ for the given Dravidian code mixed data sets. The code is provided in the Github https://github.com/suman101112/fire-2020-Dravidian-CodeMix.

* FIRE 2020: Forum for Information Retrieval Evaluation, December 16-20, 2020, Hyderabad, India 
  

Audio-Text Sentiment Analysis using Deep Robust Complementary Fusion of Multi-Features and Multi-Modalities

Apr 17, 2019
Feiyang Chen, Ziqian Luo

Sentiment analysis research has been rapidly developing in the last decade and has attracted widespread attention from academia and industry, most of which is based on text. However, the information in the real world usually comes as different modalities. In this paper, we consider the task of Multimodal Sentiment Analysis, using Audio and Text Modalities, proposed a novel fusion strategy including Multi-Feature Fusion and Multi-Modality Fusion to improve the accuracy of Audio-Text Sentiment Analysis. We call this the Deep Feature Fusion-Audio and Text Modal Fusion (DFF-ATMF) model, and the features learned from it are complementary to each other and robust. Experiments with the CMU-MOSI corpus and the recently released CMU-MOSEI corpus for Youtube video sentiment analysis show the very competitive results of our proposed model. Surprisingly, our method also achieved the state-of-the-art results in the IEMOCAP dataset, indicating that our proposed fusion strategy is also extremely generalization ability to Multimodal Emotion Recognition.

  

A Data-driven Neural Network Architecture for Sentiment Analysis

Jun 30, 2020
Erion Çano, Maurizio Morisio

The fabulous results of convolution neural networks in image-related tasks, attracted attention of text mining, sentiment analysis and other text analysis researchers. It is however difficult to find enough data for feeding such networks, optimize their parameters, and make the right design choices when constructing network architectures. In this paper we present the creation steps of two big datasets of song emotions. We also explore usage of convolution and max-pooling neural layers on song lyrics, product and movie review text datasets. Three variants of a simple and flexible neural network architecture are also compared. Our intention was to spot any important patterns that can serve as guidelines for parameter optimization of similar models. We also wanted to identify architecture design choices which lead to high performing sentiment analysis models. To this end, we conducted a series of experiments with neural architectures of various configurations. Our results indicate that parallel convolutions of filter lengths up to three are usually enough for capturing relevant text features. Also, max-pooling region size should be adapted to the length of text documents for producing the best feature maps. Top results we got are obtained with feature maps of lengths 6 to 18. An improvement on future neural network models for sentiment analysis, could be generating sentiment polarity prediction of documents using aggregation of predictions on smaller excerpt of the entire text.

* Data Technologies and Applications, Vol. 53, No. 1, pp. 2-19, 2019 
* 18 pages, 4 tables, 7 figures 
  

A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts

Jan 14, 2020
Lingyun Zhao, Lin Li, Xinhao Zheng

The emergence and rapid progress of the Internet have brought ever-increasing impact on financial domain. How to rapidly and accurately mine the key information from the massive negative financial texts has become one of the key issues for investors and decision makers. Aiming at the issue, we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media. By using pre-train model, we first study sentiment analysis, and then we consider key entity detection as a sentence matching or Machine Reading Comprehension (MRC) task in different granularity. Among them, we mainly focus on negative sentimental information. We detect the specific entity by using our approach, which is different from traditional Named Entity Recognition (NER). In addition, we also use ensemble learning to improve the performance of proposed approach. Experimental results show that the performance of our approach is generally higher than SVM, LR, NBM, and BERT for two financial sentiment analysis and key entity detection datasets.

  

Sentiment Analysis in the News

Sep 24, 2013
Alexandra Balahur, Ralf Steinberger, Mijail Kabadjov, Vanni Zavarella, Erik van der Goot, Matina Halkia, Bruno Pouliquen, Jenya Belyaeva

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles - author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which (a) we test the relative suitability of various sentiment dictionaries and (b) we attempt to separate positive or negative opinion from good or bad news. In the experiments described here, we tested whether or not subject domain-defining vocabulary should be ignored. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.

* Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC'2010), pp. 2216-2220. Valletta, Malta, 19-21 May 2010 
  

Sentiment Identification in Code-Mixed Social Media Text

Jul 04, 2017
Souvick Ghosh, Satanu Ghosh, Dipankar Das

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is a presence of sentiment in the text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach.While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labeling Facebook posts with their associated sentiments.

  

Approaches for Sentiment Analysis on Twitter: A State-of-Art study

Dec 03, 2015
Harsh Thakkar, Dhiren Patel

Microbloging is an extremely prevalent broadcast medium amidst the Internet fraternity these days. People share their opinions and sentiments about variety of subjects like products, news, institutions, etc., every day on microbloging websites. Sentiment analysis plays a key role in prediction systems, opinion mining systems, etc. Twitter, one of the microbloging platforms allows a limit of 140 characters to its users. This restriction stimulates users to be very concise about their opinion and twitter an ocean of sentiments to analyze. Twitter also provides developer friendly streaming API for data retrieval purpose allowing the analyst to search real time tweets from various users. In this paper, we discuss the state-of-art of the works which are focused on Twitter, the online social network platform, for sentiment analysis. We survey various lexical, machine learning and hybrid approaches for sentiment analysis on Twitter.

  

SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis

Sep 17, 2021
Chengxi Li, Feiyu Gao, Jiajun Bu, Lu Xu, Xiang Chen, Yu Gu, Zirui Shao, Qi Zheng, Ningyu Zhang, Yongpan Wang, Zhi Yu

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained sentiment analysis task that aims to extract aspects, classify corresponding sentiment polarities and find opinions as the causes of sentiment. The latest research tends to solve the ABSA task in a unified way with end-to-end frameworks. Yet, these frameworks get fine-tuned from downstream tasks without any task-adaptive modification. Specifically, they do not use task-related knowledge well or explicitly model relations between aspect and opinion terms, hindering them from better performance. In this paper, we propose SentiPrompt to use sentiment knowledge enhanced prompts to tune the language model in the unified framework. We inject sentiment knowledge regarding aspects, opinions, and polarities into prompt and explicitly model term relations via constructing consistency and polarity judgment templates from the ground truth triplets. Experimental results demonstrate that our approach can outperform strong baselines on Triplet Extraction, Pair Extraction, and Aspect Term Extraction with Sentiment Classification by a notable margin.

* 7pages, under blind review 
  

How Important is Syntactic Parsing Accuracy? An Empirical Evaluation on Rule-Based Sentiment Analysis

Oct 24, 2017
Carlos Gómez-Rodríguez, Iago Alonso-Alonso, David Vilares

Syntactic parsing, the process of obtaining the internal structure of sentences in natural languages, is a crucial task for artificial intelligence applications that need to extract meaning from natural language text or speech. Sentiment analysis is one example of application for which parsing has recently proven useful. In recent years, there have been significant advances in the accuracy of parsing algorithms. In this article, we perform an empirical, task-oriented evaluation to determine how parsing accuracy influences the performance of a state-of-the-art rule-based sentiment analysis system that determines the polarity of sentences from their parse trees. In particular, we evaluate the system using four well-known dependency parsers, including both current models with state-of-the-art accuracy and more innacurate models which, however, require less computational resources. The experiments show that all of the parsers produce similarly good results in the sentiment analysis task, without their accuracy having any relevant influence on the results. Since parsing is currently a task with a relatively high computational cost that varies strongly between algorithms, this suggests that sentiment analysis researchers and users should prioritize speed over accuracy when choosing a parser; and parsing researchers should investigate models that improve speed further, even at some cost to accuracy.

* 19 pages. Accepted for publication in Artificial Intelligence Review. This update only adds the DOI link to comply with journal's terms 
  
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