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"Sentiment Analysis": models, code, and papers

A Knowledge-Enhanced Adversarial Model for Cross-lingual Structured Sentiment Analysis

May 31, 2022
Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai, Jun Xiao, Liang He

Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from both industry and academia. Unfortunately, the existing structured sentiment analysis datasets refer to a few languages and are relatively small, limiting neural network models' performance. In this paper, we focus on the cross-lingual structured sentiment analysis task, which aims to transfer the knowledge from the source language to the target one. Notably, we propose a Knowledge-Enhanced Adversarial Model (\texttt{KEAM}) with both implicit distributed and explicit structural knowledge to enhance the cross-lingual transfer. First, we design an adversarial embedding adapter for learning an informative and robust representation by capturing implicit semantic information from diverse multi-lingual embeddings adaptively. Then, we propose a syntax GCN encoder to transfer the explicit semantic information (e.g., universal dependency tree) among multiple languages. We conduct experiments on five datasets and compare \texttt{KEAM} with both the supervised and unsupervised methods. The extensive experimental results show that our \texttt{KEAM} model outperforms all the unsupervised baselines in various metrics.


Does syntax matter? A strong baseline for Aspect-based Sentiment Analysis with RoBERTa

Apr 11, 2021
Junqi Dai, Hang Yan, Tianxiang Sun, Pengfei Liu, Xipeng Qiu

Aspect-based Sentiment Analysis (ABSA), aiming at predicting the polarities for aspects, is a fine-grained task in the field of sentiment analysis. Previous work showed syntactic information, e.g. dependency trees, can effectively improve the ABSA performance. Recently, pre-trained models (PTMs) also have shown their effectiveness on ABSA. Therefore, the question naturally arises whether PTMs contain sufficient syntactic information for ABSA so that we can obtain a good ABSA model only based on PTMs. In this paper, we firstly compare the induced trees from PTMs and the dependency parsing trees on several popular models for the ABSA task, showing that the induced tree from fine-tuned RoBERTa (FT-RoBERTa) outperforms the parser-provided tree. The further analysis experiments reveal that the FT-RoBERTa Induced Tree is more sentiment-word-oriented and could benefit the ABSA task. The experiments also show that the pure RoBERTa-based model can outperform or approximate to the previous SOTA performances on six datasets across four languages since it implicitly incorporates the task-oriented syntactic information.

* Accepted by NAACL 2021 

[email protected] Task 9:Sentiment Analysis of Hindi-English code mixed data

Jul 24, 2020
Avishek Garain, Sainik Kumar Mahata, Dipankar Das

Code-mixing is a phenomenon which arises mainly in multilingual societies. Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language. This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few. In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis. This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences. our username for the submission was "sainik.mahata" and team name was "JUNLP". We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task. Our approach garnered an f1-score of 66.2\% when tested using metrics prepared by the organizers of the task.


SenWave: Monitoring the Global Sentiments under the COVID-19 Pandemic

Jun 18, 2020
Qiang Yang, Hind Alamro, Somayah Albaradei, Adil Salhi, Xiaoting Lv, Changsheng Ma, Manal Alshehri, Inji Jaber, Faroug Tifratene, Wei Wang, Takashi Gojobori, Carlos M. Duarte, Xin Gao, Xiangliang Zhang

Since the first alert launched by the World Health Organization (5 January, 2020), COVID-19 has been spreading out to over 180 countries and territories. As of June 18, 2020, in total, there are now over 8,400,000 cases and over 450,000 related deaths. This causes massive losses in the economy and jobs globally and confining about 58% of the global population. In this paper, we introduce SenWave, a novel sentimental analysis work using 105+ million collected tweets and Weibo messages to evaluate the global rise and falls of sentiments during the COVID-19 pandemic. To make a fine-grained analysis on the feeling when we face this global health crisis, we annotate 10K tweets in English and 10K tweets in Arabic in 10 categories, including optimistic, thankful, empathetic, pessimistic, anxious, sad, annoyed, denial, official report, and joking. We then utilize an integrated transformer framework, called simpletransformer, to conduct multi-label sentimental classification by fine-tuning the pre-trained language model on the labeled data. Meanwhile, in order for a more complete analysis, we also translate the annotated English tweets into different languages (Spanish, Italian, and French) to generated training data for building sentiment analysis models for these languages. SenWave thus reveals the sentiment of global conversation in six different languages on COVID-19 (covering English, Spanish, French, Italian, Arabic and Chinese), followed the spread of the epidemic. The conversation showed a remarkably similar pattern of rapid rise and slow decline over time across all nations, as well as on special topics like the herd immunity strategies, to which the global conversation reacts strongly negatively. Overall, SenWave shows that optimistic and positive sentiments increased over time, foretelling a desire to seek, together, a reset for an improved COVID-19 world.


Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label Imbalance

Aug 26, 2020
Selim F. Yilmaz, E. Batuhan Kaynak, Aykut Koç, Hamdi Dibeklioğlu, Suleyman S. Kozat

We investigate cross-lingual sentiment analysis, which has attracted significant attention due to its applications in various areas including market research, politics and social sciences. In particular, we introduce a sentiment analysis framework in multi-label setting as it obeys Plutchik wheel of emotions. We introduce a novel dynamic weighting method that balances the contribution from each class during training, unlike previous static weighting methods that assign non-changing weights based on their class frequency. Moreover, we adapt the focal loss that favors harder instances from single-label object recognition literature to our multi-label setting. Furthermore, we derive a method to choose optimal class-specific thresholds that maximize the macro-f1 score in linear time complexity. Through an extensive set of experiments, we show that our method obtains the state-of-the-art performance in 7 of 9 metrics in 3 different languages using a single model compared to the common baselines and the best-performing methods in the SemEval competition. We publicly share our code for our model, which can perform sentiment analysis in 100 languages, to facilitate further research.

* 11 pages, 6 figures 

A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis

Jan 29, 2022
Xiang Chen, Xiaojun Wan

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task which aims to extract the aspects from sentences and identify their corresponding sentiments. Aspect term extraction (ATE) is the crucial step for ABSA. Due to the expensive annotation for aspect terms, we often lack labeled target domain data for fine-tuning. To address this problem, many approaches have been proposed recently to transfer common knowledge in an unsupervised way, but such methods have too many modules and require expensive multi-stage preprocessing. In this paper, we propose a simple but effective technique based on mutual information maximization, which can serve as an additional component to enhance any kind of model for cross-domain ABSA and ATE. Furthermore, we provide some analysis of this approach. Experiment results show that our proposed method outperforms the state-of-the-art methods for cross-domain ABSA by 4.32% Micro-F1 on average over 10 different domain pairs. Apart from that, our method can be extended to other sequence labeling tasks, such as named entity recognition (NER).

* 11 pages, 3 figures, 10 tables 

NITS-Hinglish-SentiMix at SemEval-2020 Task 9: Sentiment Analysis For Code-Mixed Social Media Text

Jul 23, 2020
Subhra Jyoti Baroi, Nivedita Singh, Ringki Das, Thoudam Doren Singh

Sentiment Analysis is the process of deciphering what a sentence emotes and classifying them as either positive, negative, or neutral. In recent times, India has seen a huge influx in the number of active social media users and this has led to a plethora of unstructured text data. Since the Indian population is generally fluent in both Hindi and English, they end up generating code-mixed Hinglish social media text i.e. the expressions of Hindi language, written in the Roman script alongside other English words. The ability to adequately comprehend the notions in these texts is truly necessary. Our team, rns2020 participated in Task 9 at SemEval2020 intending to design a system to carry out the sentiment analysis of code-mixed social media text. This work proposes a system named NITS-Hinglish-SentiMix to viably complete the sentiment analysis of such code-mixed Hinglish text. The proposed framework has recorded an F-Score of 0.617 on the test data.

* In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020), Barcelona, Spain, December. Association for Computational Linguistics 

A Study on Herd Behavior Using Sentiment Analysis in Online Social Network

Jul 25, 2021
Suchandra Dutta, Dhrubasish Sarkar, Sohom Roy, Dipak K. Kole, Premananda Jana

Social media platforms are thriving nowadays, so a huge volume of data is produced. As it includes brief and clear statements, millions of people post their thoughts on microblogging sites every day. This paper represents and analyze the capacity of diverse strategies to volumetric, delicate, and social networks to predict critical opinions from online social networking sites. In the exploration of certain searching for relevant, the thoughts of people play a crucial role. Social media becomes a good outlet since the last decades to share the opinions globally. Sentiment analysis as well as opinion mining is a tool that is used to extract the opinions or thoughts of the common public. An occurrence in one place, be it economic, political, or social, may trigger large-scale chain public reaction across many other sites in an increasingly interconnected world. This study demonstrates the evaluation of sentiment analysis techniques using social media contents and creating the association between subjectivity with herd behavior and clustering coefficient as well as tries to predict the election result (2021 election in West Bengal). This is an implementation of sentiment analysis targeted at estimating the results of an upcoming election by assessing the public's opinion across social media. This paper also has a short discussion section on the usefulness of the idea in other fields.

* 2021 International Conference on Communication, Control and Information Sciences (ICCISc), Idukki, India 

ScaleVLAD: Improving Multimodal Sentiment Analysis via Multi-Scale Fusion of Locally Descriptors

Dec 02, 2021
Huaishao Luo, Lei Ji, Yanyong Huang, Bin Wang, Shenggong Ji, Tianrui Li

Fusion technique is a key research topic in multimodal sentiment analysis. The recent attention-based fusion demonstrates advances over simple operation-based fusion. However, these fusion works adopt single-scale, i.e., token-level or utterance-level, unimodal representation. Such single-scale fusion is suboptimal because that different modality should be aligned with different granularities. This paper proposes a fusion model named ScaleVLAD to gather multi-Scale representation from text, video, and audio with shared Vectors of Locally Aggregated Descriptors to improve unaligned multimodal sentiment analysis. These shared vectors can be regarded as shared topics to align different modalities. In addition, we propose a self-supervised shifted clustering loss to keep the fused feature differentiation among samples. The backbones are three Transformer encoders corresponding to three modalities, and the aggregated features generated from the fusion module are feed to a Transformer plus a full connection to finish task predictions. Experiments on three popular sentiment analysis benchmarks, IEMOCAP, MOSI, and MOSEI, demonstrate significant gains over baselines.


Sentiment Analysis of Financial News Articles using Performance Indicators

Nov 25, 2018
Srikumar Krishnamoorthy

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.

* Knowledge and Information Systems Nov 2017