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

Continual Learning with Knowledge Transfer for Sentiment Classification

Dec 18, 2021
Zixuan Ke, Bing Liu, Hao Wang, Lei Shu

This paper studies continual learning (CL) for sentiment classification (SC). In this setting, the CL system learns a sequence of SC tasks incrementally in a neural network, where each task builds a classifier to classify the sentiment of reviews of a particular product category or domain. Two natural questions are: Can the system transfer the knowledge learned in the past from the previous tasks to the new task to help it learn a better model for the new task? And, can old models for previous tasks be improved in the process as well? This paper proposes a novel technique called KAN to achieve these objectives. KAN can markedly improve the SC accuracy of both the new task and the old tasks via forward and backward knowledge transfer. The effectiveness of KAN is demonstrated through extensive experiments.

* ECML-PKDD 2020 

  Access Paper or Ask Questions

A Sentiment Analysis Dataset for Trustworthiness Evaluation

Aug 30, 2021
Lijie Wang, Hao Liu, Shuyuan Peng, Hongxuan Tang, Xinyan Xiao, Ying Chen, Hua Wu

While deep learning models have greatly improved the performance of most artificial intelligence tasks, they are often criticized to be untrustworthy due to the black-box problem. Consequently, many works have been proposed to study the trustworthiness of deep learning. However, as most open datasets are designed for evaluating the accuracy of model outputs, there is still a lack of appropriate datasets for evaluating the inner workings of neural networks. The lack of datasets obviously hinders the development of trustworthiness research. Therefore, in order to systematically evaluate the factors for building trustworthy systems, we propose a novel and well-annotated sentiment analysis dataset to evaluate robustness and interpretability. To evaluate these factors, our dataset contains diverse annotations about the challenging distribution of instances, manual adversarial instances and sentiment explanations. Several evaluation metrics are further proposed for interpretability and robustness. Based on the dataset and metrics, we conduct comprehensive comparisons for the trustworthiness of three typical models, and also study the relations between accuracy, robustness and interpretability. We release this trustworthiness evaluation dataset at \url{https://github/xyz} and hope our work can facilitate the progress on building more trustworthy systems for real-world applications.

  Access Paper or Ask Questions

Multilingual Visual Sentiment Concept Matching

Jun 07, 2016
Nikolaos Pappas, Miriam Redi, Mercan Topkara, Brendan Jou, Hongyi Liu, Tao Chen, Shih-Fu Chang

The impact of culture in visual emotion perception has recently captured the attention of multimedia research. In this study, we pro- vide powerful computational linguistics tools to explore, retrieve and browse a dataset of 16K multilingual affective visual concepts and 7.3M Flickr images. First, we design an effective crowdsourc- ing experiment to collect human judgements of sentiment connected to the visual concepts. We then use word embeddings to repre- sent these concepts in a low dimensional vector space, allowing us to expand the meaning around concepts, and thus enabling insight about commonalities and differences among different languages. We compare a variety of concept representations through a novel evaluation task based on the notion of visual semantic relatedness. Based on these representations, we design clustering schemes to group multilingual visual concepts, and evaluate them with novel metrics based on the crowdsourced sentiment annotations as well as visual semantic relatedness. The proposed clustering framework enables us to analyze the full multilingual dataset in-depth and also show an application on a facial data subset, exploring cultural in- sights of portrait-related affective visual concepts.

* Proceedings ICMR '16 Proceedings of the 2016 ACM on International Conference on Multimedia Retrieval Pages 151-158 

  Access Paper or Ask Questions

Sentiment Dynamics of Success: Fractal Scaling of Story Arcs Predicts Reader Preferences

Dec 14, 2021
Yuri Bizzoni, Telma Peura, Mads R. Thomsen, Kristoffer Nielbo

We explore the correlation between the sentiment arcs of H. C. Andersen's fairy tales and their popularity, measured as their average score on the platform GoodReads. Specifically, we do not conceive a story's overall sentimental trend as predictive \textit{per se}, but we focus on its coherence and predictability over time as represented by the arc's Hurst exponent. We find that degrading Hurst values tend to imply degrading quality scores, while a Hurst exponent between .55 and .65 might indicate a "sweet spot" for literary appreciation.

  Access Paper or Ask Questions

Augmenting semantic lexicons using word embeddings and transfer learning

Sep 18, 2021
Thayer Alshaabi, Colin Van Oort, Mikaela Fudolig, Michael V. Arnold, Christopher M. Danforth, Peter Sheridan Dodds

Sentiment-aware intelligent systems are essential to a wide array of applications including marketing, political campaigns, recommender systems, behavioral economics, social psychology, and national security. These sentiment-aware intelligent systems are driven by language models which broadly fall into two paradigms: 1. Lexicon-based and 2. Contextual. Although recent contextual models are increasingly dominant, we still see demand for lexicon-based models because of their interpretability and ease of use. For example, lexicon-based models allow researchers to readily determine which words and phrases contribute most to a change in measured sentiment. A challenge for any lexicon-based approach is that the lexicon needs to be routinely expanded with new words and expressions. Crowdsourcing annotations for semantic dictionaries may be an expensive and time-consuming task. Here, we propose two models for predicting sentiment scores to augment semantic lexicons at a relatively low cost using word embeddings and transfer learning. Our first model establishes a baseline employing a simple and shallow neural network initialized with pre-trained word embeddings using a non-contextual approach. Our second model improves upon our baseline, featuring a deep Transformer-based network that brings to bear word definitions to estimate their lexical polarity. Our evaluation shows that both models are able to score new words with a similar accuracy to reviewers from Amazon Mechanical Turk, but at a fraction of the cost.

* 16 pages, 9 figures 

  Access Paper or Ask Questions

ExCode-Mixed: Explainable Approaches towards Sentiment Analysis on Code-Mixed Data using BERT models

Sep 25, 2021
Aman Priyanshu, Aleti Vardhan, Sudarshan Sivakumar, Supriti Vijay, Nipuna Chhabra

The increasing use of social media sites in countries like India has given rise to large volumes of code-mixed data. Sentiment analysis of this data can provide integral insights into people's perspectives and opinions. Developing robust explainability techniques which explain why models make their predictions becomes essential. In this paper, we propose an adequate methodology to integrate explainable approaches into code-mixed sentiment analysis.

* 3 pages, 1 figure 

  Access Paper or Ask Questions

Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet

Sep 23, 2013
Marco Guerini, Lorenzo Gatti, Marco Turchi

Assigning a positive or negative score to a word out of context (i.e. a word's prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of SentiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-of-the-art approach in computing words' prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered.

* To appear in Proceedings of EMNLP 2013 

  Access Paper or Ask Questions

Text Length Adaptation in Sentiment Classification

Sep 18, 2019
Reinald Kim Amplayo, Seonjae Lim, Seung-won Hwang

Can a text classifier generalize well for datasets where the text length is different? For example, when short reviews are sentiment-labeled, can these transfer to predict the sentiment of long reviews (i.e., short to long transfer), or vice versa? While unsupervised transfer learning has been well-studied for cross domain/lingual transfer tasks, Cross Length Transfer (CLT) has not yet been explored. One reason is the assumption that length difference is trivially transferable in classification. We show that it is not, because short/long texts differ in context richness and word intensity. We devise new benchmark datasets from diverse domains and languages, and show that existing models from similar tasks cannot deal with the unique challenge of transferring across text lengths. We introduce a strong baseline model called BaggedCNN that treats long texts as bags containing short texts. We propose a state-of-the-art CLT model called Length Transfer Networks (LeTraNets) that introduces a two-way encoding scheme for short and long texts using multiple training mechanisms. We test our models and find that existing models perform worse than the BaggedCNN baseline, while LeTraNets outperforms all models.

* ACML 2019 

  Access Paper or Ask Questions

LiSSS: A toy corpus of Literary Spanish Sentences Sentiment for Emotions Detection

May 17, 2020
Juan-Manuel Torres-Moreno, Luis-Gil Moreno-Jiménez

In this work we present a new and small corpus in the area of Computational Creativity (CC), the Literary Sentiment Sentence Spanish Corpus (LISSS). We address this corpus of literary sentences in order to evaluate algorithms of sentiment classification and emotions detection. We have constitute it by manually classifying its sentences in five emotions: Love, Fear, Happiness, Anger and Sadness/Pain. We also present some baseline classification algorithms applied on our corpus. The LISSS corpus will be available to the community as a free resource to evaluate or create CC algorithms.

* 8 pages, 3 tables 

  Access Paper or Ask Questions