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"Sentiment Analysis": models, code, and papers

Analyzing users' sentiment towards popular consumer industries and brands on Twitter

Sep 21, 2017
Guoning Hu, Preeti Bhargava, Saul Fuhrmann, Sarah Ellinger, Nemanja Spasojevic

Social media serves as a unified platform for users to express their thoughts on subjects ranging from their daily lives to their opinion on consumer brands and products. These users wield an enormous influence in shaping the opinions of other consumers and influence brand perception, brand loyalty and brand advocacy. In this paper, we analyze the opinion of 19M Twitter users towards 62 popular industries, encompassing 12,898 enterprise and consumer brands, as well as associated subject matter topics, via sentiment analysis of 330M tweets over a period spanning a month. We find that users tend to be most positive towards manufacturing and most negative towards service industries. In addition, they tend to be more positive or negative when interacting with brands than generally on Twitter. We also find that sentiment towards brands within an industry varies greatly and we demonstrate this using two industries as use cases. In addition, we discover that there is no strong correlation between topic sentiments of different industries, demonstrating that topic sentiments are highly dependent on the context of the industry that they are mentioned in. We demonstrate the value of such an analysis in order to assess the impact of brands on social media. We hope that this initial study will prove valuable for both researchers and companies in understanding users' perception of industries, brands and associated topics and encourage more research in this field.

* 2017 IEEE International Conference on Data Mining Workshops (ICDMW 2017) 
* 8 pages, 11 figures, 1 table, 2017 IEEE International Conference on Data Mining Workshops (ICDMW 2017), ICDM Sentiment Elicitation from Natural Text for Information Retrieval and Extraction (ICDM SENTIRE) 2017 workshop 
  
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Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis

Aug 06, 2018
Hai Pham, Thomas Manzini, Paul Pu Liang, Barnabas Poczos

Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities. The central challenge in multimodal learning involves learning representations that can process and relate information from multiple modalities. In this paper, we propose two methods for unsupervised learning of joint multimodal representations using sequence to sequence (Seq2Seq) methods: a \textit{Seq2Seq Modality Translation Model} and a \textit{Hierarchical Seq2Seq Modality Translation Model}. We also explore multiple different variations on the multimodal inputs and outputs of these seq2seq models. Our experiments on multimodal sentiment analysis using the CMU-MOSI dataset indicate that our methods learn informative multimodal representations that outperform the baselines and achieve improved performance on multimodal sentiment analysis, specifically in the Bimodal case where our model is able to improve F1 Score by twelve points. We also discuss future directions for multimodal Seq2Seq methods.

* 8 pages of content, 11 pages total, 2 figures. Published as a workshop paper at ACL 2018, Proceedings of Grand Challenge and Workshop on Human Multimodal Language (Challenge-HML). 2018 
  
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Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives

Sep 21, 2018
Jing Han, Zixing Zhang, Nicholas Cummins, Björn Schuller

Over the past few years, adversarial training has become an extremely active research topic and has been successfully applied to various Artificial Intelligence (AI) domains. As a potentially crucial technique for the development of the next generation of emotional AI systems, we herein provide a comprehensive overview of the application of adversarial training to affective computing and sentiment analysis. Various representative adversarial training algorithms are explained and discussed accordingly, aimed at tackling diverse challenges associated with emotional AI systems. Further, we highlight a range of potential future research directions. We expect that this overview will help facilitate the development of adversarial training for affective computing and sentiment analysis in both the academic and industrial communities.

  
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A SentiWordNet Strategy for Curriculum Learning in Sentiment Analysis

May 10, 2020
Vijjini Anvesh Rao, Kaveri Anuranjana, Radhika Mamidi

Curriculum Learning (CL) is the idea that learning on a training set sequenced or ordered in a manner where samples range from easy to difficult, results in an increment in performance over otherwise random ordering. The idea parallels cognitive science's theory of how human brains learn, and that learning a difficult task can be made easier by phrasing it as a sequence of easy to difficult tasks. This idea has gained a lot of traction in machine learning and image processing for a while and recently in Natural Language Processing (NLP). In this paper, we apply the ideas of curriculum learning, driven by SentiWordNet in a sentiment analysis setting. In this setting, given a text segment, our aim is to extract its sentiment or polarity. SentiWordNet is a lexical resource with sentiment polarity annotations. By comparing performance with other curriculum strategies and with no curriculum, the effectiveness of the proposed strategy is presented. Convolutional, Recurrence, and Attention-based architectures are employed to assess this improvement. The models are evaluated on a standard sentiment dataset, Stanford Sentiment Treebank.

* Accepted Short Paper at 25th International Conference on Applications of Natural Language to Information Systems, June 2020, DFKI Saarbr\"ucken, Germany 
  
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Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis

Nov 30, 2019
Zhongkai Sun, Prathusha Sarma, William Sethares, Yingyu Liang

Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video. This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.

  
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Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis

Sep 30, 2020
Xiaoyu Xing, Zhijing Jin, Di Jin, Bingning Wang, Qi Zhang, Xuanjing Huang

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment towards a specific aspect in the text. However, existing ABSA test sets cannot be used to probe whether a model can distinguish the sentiment of the target aspect from the non-target aspects. To solve this problem, we develop a simple but effective approach to enrich ABSA test sets. Specifically, we generate new examples to disentangle the confounding sentiments of the non-target aspects from the target aspect's sentiment. Based on the SemEval 2014 dataset, we construct the Aspect Robustness Test Set (ARTS) as a comprehensive probe of the aspect robustness of ABSA models. Over 92% data of ARTS show high fluency and desired sentiment on all aspects by human evaluation. Using ARTS, we analyze the robustness of nine ABSA models, and observe, surprisingly, that their accuracy drops by up to 69.73%. Our code and new test set are available at https://github.com/zhijing-jin/ARTS_TestSet.

* EMNLP 2020, long paper 
  
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Sentiment Analysis for Open Domain Conversational Agent

Jan 03, 2021
Mohamad Alissa, Issa Haddad, Jonathan Meyer, Jade Obeid, Kostis Vilaetis, Nicolas Wiecek, Sukrit Wongariyakavee

The applicability of common sentiment analysis models to open domain human robot interaction is investigated within this paper. The models are used on a dataset specific to user interaction with the Alana system (a Alexa prize system) in order to determine which would be more appropriate for the task of identifying sentiment when a user interacts with a non-human driven socialbot. With the identification of a model, various improvements are attempted and detailed prior to integration into the Alana system. The study showed that a Random Forest Model with 25 trees trained on the dataset specific to user interaction with the Alana system combined with the dataset present in NLTK Vader outperforms other models. The new system (called 'Rob') matches it's output utterance sentiment with the user's utterance sentiment. This method is expected to improve user experience because it builds upon the overall sentiment detection which makes it seem that new system sympathises with user feelings. Furthermore, the results obtained from the user feedback confirms our expectation.

* 9 pages, 3 figures 
  
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Crowdsourcing for Beyond Polarity Sentiment Analysis A Pure Emotion Lexicon

Oct 04, 2017
Giannis Haralabopoulos, Elena Simperl

Sentiment analysis aims to uncover emotions conveyed through information. In its simplest form, it is performed on a polarity basis, where the goal is to classify information with positive or negative emotion. Recent research has explored more nuanced ways to capture emotions that go beyond polarity. For these methods to work, they require a critical resource: a lexicon that is appropriate for the task at hand, in terms of the range of emotions it captures diversity. In the past, sentiment analysis lexicons have been created by experts, such as linguists and behavioural scientists, with strict rules. Lexicon evaluation was also performed by experts or gold standards. In our paper, we propose a crowdsourcing method for lexicon acquisition, which is scalable, cost-effective, and doesn't require experts or gold standards. We also compare crowd and expert evaluations of the lexicon, to assess the overall lexicon quality, and the evaluation capabilities of the crowd.

* Keywords: Beyond Polarity, Pure Sentiment, Crowdsourcing, Sentiment Analysis, Lexicon Acquisition, Reddit, Twitter, Brexit [19 pages, 6 figures, 4 tables] 
  
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