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"Sentiment": models, code, and papers

W2VLDA: Almost Unsupervised System for Aspect Based Sentiment Analysis

Jul 18, 2017
Aitor García-Pablos, Montse Cuadros, German Rigau

With the increase of online customer opinions in specialised websites and social networks, the necessity of automatic systems to help to organise and classify customer reviews by domain-specific aspect/categories and sentiment polarity is more important than ever. Supervised approaches to Aspect Based Sentiment Analysis obtain good results for the domain/language their are trained on, but having manually labelled data for training supervised systems for all domains and languages are usually very costly and time consuming. In this work we describe W2VLDA, an almost unsupervised system based on topic modelling, that combined with some other unsupervised methods and a minimal configuration, performs aspect/category classifiation, aspect-terms/opinion-words separation and sentiment polarity classification for any given domain and language. We evaluate the performance of the aspect and sentiment classification in the multilingual SemEval 2016 task 5 (ABSA) dataset. We show competitive results for several languages (English, Spanish, French and Dutch) and domains (hotels, restaurants, electronic-devices).

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A Position Aware Decay Weighted Network for Aspect based Sentiment Analysis

May 03, 2020
Avinash Madasu, Vijjini Anvesh Rao

Aspect Based Sentiment Analysis (ABSA) is the task of identifying sentiment polarity of a text given another text segment or aspect. In ABSA, a text can have multiple sentiments depending upon each aspect. Aspect Term Sentiment Analysis (ATSA) is a subtask of ABSA, in which aspect terms are contained within the given sentence. Most of the existing approaches proposed for ATSA, incorporate aspect information through a different subnetwork thereby overlooking the advantage of aspect terms' presence within the sentence. In this paper, we propose a model that leverages the positional information of the aspect. The proposed model introduces a decay mechanism based on position. This decay function mandates the contribution of input words for ABSA. The contribution of a word declines as farther it is positioned from the aspect terms in the sentence. The performance is measured on two standard datasets from SemEval 2014 Task 4. In comparison with recent architectures, the effectiveness of the proposed model is demonstrated.

* Accepted Full Paper at 25th International Conference on Applications of Natural Language to Information Systems, June 2020, DFKI Saarbr\"ucken, Germany 

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Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models

Apr 22, 2022
Abdulaziz A. Almuzaini, Vivek K. Singh

Sentiment detection is an important building block for multiple information retrieval tasks such as product recommendation, cyberbullying detection, and misinformation detection. Unsurprisingly, multiple commercial APIs, each with different levels of accuracy and fairness, are now available for sentiment detection. While combining inputs from multiple modalities or black-box models for increasing accuracy is commonly studied in multimedia computing literature, there has been little work on combining different modalities for increasing fairness of the resulting decision. In this work, we audit multiple commercial sentiment detection APIs for the gender bias in two actor news headlines settings and report on the level of bias observed. Next, we propose a "Flexible Fair Regression" approach, which ensures satisfactory accuracy and fairness by jointly learning from multiple black-box models. The results pave way for fair yet accurate sentiment detectors for multiple applications.

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GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis

Sep 25, 2020
Huaishao Luo, Lei Ji, Tianrui Li, Nan Duan, Daxin Jiang

In this paper, we focus on the imbalance issue, which is rarely studied in aspect term extraction and aspect sentiment classification when regarding them as sequence labeling tasks. Besides, previous works usually ignore the interaction between aspect terms when labeling polarities. We propose a GRadient hArmonized and CascadEd labeling model (GRACE) to solve these problems. Specifically, a cascaded labeling module is developed to enhance the interchange between aspect terms and improve the attention of sentiment tokens when labeling sentiment polarities. The polarities sequence is designed to depend on the generated aspect terms labels. To alleviate the imbalance issue, we extend the gradient harmonized mechanism used in object detection to the aspect-based sentiment analysis by adjusting the weight of each label dynamically. The proposed GRACE adopts a post-pretraining BERT as its backbone. Experimental results demonstrate that the proposed model achieves consistency improvement on multiple benchmark datasets and generates state-of-the-art results.

* to appear in Findings of EMNLP 2020 

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yosm: A new yoruba sentiment corpus for movie reviews

Apr 20, 2022
Iyanuoluwa Shode, David Ifeoluwa Adelani, Anna Feldman

A movie that is thoroughly enjoyed and recommended by an individual might be hated by another. One characteristic of humans is the ability to have feelings which could be positive or negative. To automatically classify and study human feelings, an aspect of natural language processing, sentiment analysis and opinion mining were designed to understand human feelings regarding several issues which could affect a product, a social media platforms, government, or societal discussions or even movies. Several works on sentiment analysis have been done on high resource languages while low resources languages like Yoruba have been sidelined. Due to the scarcity of datasets and linguistic architectures that will suit low resource languages, African languages "low resource languages" have been ignored and not fully explored. For this reason, our attention is placed on Yoruba to explore sentiment analysis on reviews of Nigerian movies. The data comprised 1500 movie reviews that were sourced from IMDB, Rotten Tomatoes, Letterboxd, Cinemapointer and Nollyrated. We develop sentiment classification models using the state-of-the-art pre-trained language models like mBERT and AfriBERTa to classify the movie reviews.

* Accepted to AfricaNLP Workshop @ICLR 2022 

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Adversarial Training for Aspect-Based Sentiment Analysis with BERT

Jan 31, 2020
Akbar Karimi, Leonardo Rossi, Andrea Prati, Katharina Full

Aspect-Based Sentiment Analysis (ABSA) deals with the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. After improving the results of post-trained BERT by an ablation study, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training in ABSA. The proposed model outperforms post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA.

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A Combined CNN and LSTM Model for Arabic Sentiment Analysis

Jul 22, 2018
Abdulaziz M. Alayba, Vasile Palade, Matthew England, Rahat Iqbal

Deep neural networks have shown good data modelling capabilities when dealing with challenging and large datasets from a wide range of application areas. Convolutional Neural Networks (CNNs) offer advantages in selecting good features and Long Short-Term Memory (LSTM) networks have proven good abilities of learning sequential data. Both approaches have been reported to provide improved results in areas such image processing, voice recognition, language translation and other Natural Language Processing (NLP) tasks. Sentiment classification for short text messages from Twitter is a challenging task, and the complexity increases for Arabic language sentiment classification tasks because Arabic is a rich language in morphology. In addition, the availability of accurate pre-processing tools for Arabic is another current limitation, along with limited research available in this area. In this paper, we investigate the benefits of integrating CNNs and LSTMs and report obtained improved accuracy for Arabic sentiment analysis on different datasets. Additionally, we seek to consider the morphological diversity of particular Arabic words by using different sentiment classification levels.

* Proc. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. CD-MAKE 2018. Lecture Notes in Computer Science, vol 11015, pp. 179-191. Springer, Cham 
* Authors accepted version of submission for CD-MAKE 2018 

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Towards A Sentiment Analyzer for Low-Resource Languages

Nov 12, 2020
Dian Indriani, Arbi Haza Nasution, Winda Monika, Salhazan Nasution

Twitter is one of the top influenced social media which has a million number of active users. It is commonly used for microblogging that allows users to share messages, ideas, thoughts and many more. Thus, millions interaction such as short messages or tweets are flowing around among the twitter users discussing various topics that has been happening world-wide. This research aims to analyse a sentiment of the users towards a particular trending topic that has been actively and massively discussed at that time. We chose a hashtag \textit{\#kpujangancurang} that was the trending topic during the Indonesia presidential election in 2019. We use the hashtag to obtain a set of data from Twitter to analyse and investigate further the positive or the negative sentiment of the users from their tweets. This research utilizes rapid miner tool to generate the twitter data and comparing Naive Bayes, K-Nearest Neighbor, Decision Tree, and Multi-Layer Perceptron classification methods to classify the sentiment of the twitter data. There are overall 200 labeled data in this experiment. Overall, Naive Bayes and Multi-Layer Perceptron classification outperformed the other two methods on 11 experiments with different size of training-testing data split. The two classifiers are potential to be used in creating sentiment analyzer for low-resource languages with small corpus.

* Accepted to be published in Proceedings of International Conference on Smart Computing and Cyber Security (SMARTCYBER 2020) 

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Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

Sep 25, 2019
Mengting Hu, Shiwan Zhao, Honglei Guo, Renhong Cheng, Zhong Su

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence. Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations. The attention is expected to concentrate on opinion words for accurate sentiment prediction. However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects. In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction. Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model. We further detect the opinion snippet by self-critical reinforcement learning. Especially, experimental results demonstrate the effectiveness of our method and prove that our hard-selection approach outperforms soft-selection approaches when handling multi-aspect sentences.

* Accepted by CoNLL 2019 

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C1 at SemEval-2020 Task 9: SentiMix: Sentiment Analysis for Code-Mixed Social Media Text using Feature Engineering

Aug 09, 2020
Laksh Advani, Clement Lu, Suraj Maharjan

In today's interconnected and multilingual world, code-mixing of languages on social media is a common occurrence. While many Natural Language Processing (NLP) tasks like sentiment analysis are mature and well designed for monolingual text, techniques to apply these tasks to code-mixed text still warrant exploration. This paper describes our feature engineering approach to sentiment analysis in code-mixed social media text for SemEval-2020 Task 9: SentiMix. We tackle this problem by leveraging a set of hand-engineered lexical, sentiment, and metadata features to design a classifier that can disambiguate between "positive", "negative" and "neutral" sentiment. With this model, we are able to obtain a weighted F1 score of 0.65 for the "Hinglish" task and 0.63 for the "Spanglish" tasks

* SemEval-2020 Task 9 

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