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"Sentiment": models, code, and papers

Attentional Encoder Network for Targeted Sentiment Classification

Apr 01, 2019
Youwei Song, Jiahai Wang, Tao Jiang, Zhiyue Liu, Yanghui Rao

Targeted sentiment classification aims at determining the sentimental tendency towards specific targets. Most of the previous approaches model context and target words with RNN and attention. However, RNNs are difficult to parallelize and truncated backpropagation through time brings difficulty in remembering long-term patterns. To address this issue, this paper proposes an Attentional Encoder Network (AEN) which eschews recurrence and employs attention based encoders for the modeling between context and target. We raise the label unreliability issue and introduce label smoothing regularization. We also apply pre-trained BERT to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the effectiveness and lightweight of our model.

* 7 pages 

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Japanese Sentiment Classification using a Tree-Structured Long Short-Term Memory with Attention

Sep 30, 2018
Ryosuke Miyazaki, Mamoru Komachi

Previous approaches to training syntax-based sentiment classification models required phrase-level annotated corpora, which are not readily available in many languages other than English. Thus, we propose the use of tree-structured Long Short-Term Memory with an attention mechanism that pays attention to each subtree of the parse tree. Experimental results indicate that our model achieves the state-of-the-art performance in a Japanese sentiment classification task.

* 10 pages; PACLIC 2018 

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Computing trading strategies based on financial sentiment data using evolutionary optimization

Apr 12, 2015
Ronald Hochreiter

In this paper we apply evolutionary optimization techniques to compute optimal rule-based trading strategies based on financial sentiment data. The sentiment data was extracted from the social media service StockTwits to accommodate the level of bullishness or bearishness of the online trading community towards certain stocks. Numerical results for all stocks from the Dow Jones Industrial Average (DJIA) index are presented and a comparison to classical risk-return portfolio selection is provided.


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Comparative Sentiment Analysis of App Reviews

Jun 17, 2020
Sakshi Ranjan, Subhankar Mishra

Google app market captures the school of thought of users via ratings and text reviews. The critique's viewpoint regarding an app is proportional to their satisfaction level. Consequently, this helps other users to gain insights before downloading or purchasing the apps. The potential information from the reviews can't be extracted manually, due to its exponential growth. Sentiment analysis, by machine learning algorithms employing NLP, is used to explicitly uncover and interpret the emotions. This study aims to perform the sentiment classification of the app reviews and identify the university students' behavior towards the app market. We applied machine learning algorithms using the TF-IDF text representation scheme and the performance was evaluated on the ensemble learning method. Our model was trained on Google reviews and tested on students' reviews. SVM recorded the maximum accuracy(93.37\%), F-score(0.88) on tri-gram + TF-IDF scheme. Bagging enhanced the performance of LR and NB with accuracy of 87.80\% and 85.5\% respectively.

* 10 pages, 7 figures, Accepted to the 11th ICCCNT, 2020, IIT KGP 

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Fast Approach to Build an Automatic Sentiment Annotator for Legal Domain using Transfer Learning

Oct 03, 2018
Viraj Gamage, Menuka Warushavithana, Nisansa de Silva, Amal Shehan Perera, Gathika Ratnayaka, Thejan Rupasinghe

This study proposes a novel way of identifying the sentiment of the phrases used in the legal domain. The added complexity of the language used in law, and the inability of the existing systems to accurately predict the sentiments of words in law are the main motivations behind this study. This is a transfer learning approach, which can be used for other domain adaptation tasks as well. The proposed methodology achieves an improvement of over 6\% compared to the source model's accuracy in the legal domain.

* 9 pages, 3 figures 

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Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter

May 03, 2017
Alon Rozental, Daniel Fleischer

This paper describes the Amobee sentiment analysis system, adapted to compete in SemEval 2017 task 4. The system consists of two parts: a supervised training of RNN models based on a Twitter sentiment treebank, and the use of feedforward NN, Naive Bayes and logistic regression classifiers to produce predictions for the different sub-tasks. The algorithm reached the 3rd place on the 5-label classification task (sub-task C).

* 6 pages, accepted to the 11th International Workshop on Semantic Evaluation (SemEval-2017) 

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Fast and accurate sentiment classification using an enhanced Naive Bayes model

Sep 16, 2013
Vivek Narayanan, Ishan Arora, Arjun Bhatia

We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset.

* Intelligent Data Engineering and Automated Learning IDEAL 2013 Lecture Notes in Computer Science Volume 8206, 2013, pp 194-201 
* 8 pages, 2 figures 

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A Comparative Study on Linguistic Feature Selection in Sentiment Polarity Classification

Nov 04, 2013
Zitao Liu

Sentiment polarity classification is perhaps the most widely studied topic. It classifies an opinionated document as expressing a positive or negative opinion. In this paper, using movie review dataset, we perform a comparative study with different single kind linguistic features and the combinations of these features. We find that the classic topic-based classifier(Naive Bayes and Support Vector Machine) do not perform as well on sentiment polarity classification. And we find that with some combination of different linguistic features, the classification accuracy can be boosted a lot. We give some reasonable explanations about these boosting outcomes.

* arXiv admin note: text overlap with arXiv:cs/0205070 by other authors 

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Fortia-FBK at SemEval-2017 Task 5: Bullish or Bearish? Inferring Sentiment towards Brands from Financial News Headlines

Apr 04, 2017
Youness Mansar, Lorenzo Gatti, Sira Ferradans, Marco Guerini, Jacopo Staiano

In this paper, we describe a methodology to infer Bullish or Bearish sentiment towards companies/brands. More specifically, our approach leverages affective lexica and word embeddings in combination with convolutional neural networks to infer the sentiment of financial news headlines towards a target company. Such architecture was used and evaluated in the context of the SemEval 2017 challenge (task 5, subtask 2), in which it obtained the best performance.

* 6 pages, 1 figure; accepted for publication at the International Workshop on Semantic Evaluation (SemEval-2017) to be held in conjunction with ACL 2017 

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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 

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