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"Sentiment": models, code, and papers

Insight from NLP Analysis: COVID-19 Vaccines Sentiments on Social Media

Jun 08, 2021
Tao Na, Wei Cheng, Dongming Li, Wanyu Lu, Hongjiang Li

Social media is an appropriate source for analyzing public attitudes towards the COVID-19 vaccine and various brands. Nevertheless, there are few relevant studies. In the research, we collected tweet posts by the UK and US residents from the Twitter API during the pandemic and designed experiments to answer three main questions concerning vaccination. To get the dominant sentiment of the civics, we performed sentiment analysis by VADER and proposed a new method that can count the individual's influence. This allows us to go a step further in sentiment analysis and explain some of the fluctuations in the data changing. The results indicated that celebrities could lead the opinion shift on social media in vaccination progress. Moreover, at the peak, nearly 40\% of the population in both countries have a negative attitude towards COVID-19 vaccines. Besides, we investigated how people's opinions toward different vaccine brands are. We found that the Pfizer vaccine enjoys the most popular among people. By applying the sentiment analysis tool, we discovered most people hold positive views toward the COVID-19 vaccine manufactured by most brands. In the end, we carried out topic modelling by using the LDA model. We found residents in the two countries are willing to share their views and feelings concerning the vaccine. Several death cases have occurred after vaccination. Due to these negative events, US residents are more worried about the side effects and safety of the vaccine.


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Combining Context-Free and Contextualized Representations for Arabic Sarcasm Detection and Sentiment Identification

Mar 09, 2021
Amey Hengle, Atharva Kshirsagar, Shaily Desai, Manisha Marathe

Since their inception, transformer-based language models have led to impressive performance gains across multiple natural language processing tasks. For Arabic, the current state-of-the-art results on most datasets are achieved by the AraBERT language model. Notwithstanding these recent advancements, sarcasm and sentiment detection persist to be challenging tasks in Arabic, given the language's rich morphology, linguistic disparity and dialectal variations. This paper proffers team SPPU-AASM's submission for the WANLP ArSarcasm shared-task 2021, which centers around the sarcasm and sentiment polarity detection of Arabic tweets. The study proposes a hybrid model, combining sentence representations from AraBERT with static word vectors trained on Arabic social media corpora. The proposed system achieves a F1-sarcastic score of 0.62 and a F-PN score of 0.715 for the sarcasm and sentiment detection tasks, respectively. Simulation results show that the proposed system outperforms multiple existing approaches for both the tasks, suggesting that the amalgamation of context-free and context-dependent text representations can help capture complementary facets of word meaning in Arabic. The system ranked second and tenth in the respective sub-tasks of sarcasm detection and sentiment identification.

* 7 pages, 1 figure, The Sixth Arabic Natural Language Processing Workshop. (WANLP 2021), held in conjunction with EACL 2021 

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A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine

Feb 17, 2019
Omar Al-Harbi

Unlike other languages, the Arabic language has a morphological complexity which makes the Arabic sentiment analysis is a challenging task. Moreover, the presence of the dialects in the Arabic texts have made the sentiment analysis task is more challenging, due to the absence of specific rules that govern the writing or speaking system. Generally, one of the problems of sentiment analysis is the high dimensionality of the feature vector. To resolve this problem, many feature selection methods have been proposed. In contrast to the dialectal Arabic language, these selection methods have been investigated widely for the English language. This work investigated the effect of feature selection methods and their combinations on dialectal Arabic sentiment classification. The feature selection methods are Information Gain (IG), Correlation, Support Vector Machine (SVM), Gini Index (GI), and Chi-Square. A number of experiments were carried out on dialectical Jordanian reviews with using an SVM classifier. Furthermore, the effect of different term weighting schemes, stemmers, stop words removal, and feature models on the performance were investigated. The experimental results showed that the best performance of the SVM classifier was obtained after the SVM and correlation feature selection methods had been combined with the uni-gram model.

* IJCSNS International Journal of Computer Science and Network Security, VOL.19 No.1, January 2019, 167-176 
* 10 pages 

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Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text

Nov 02, 2016
Ameya Prabhu, Aditya Joshi, Manish Shrivastava, Vasudeva Varma

Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. In this paper, we introduce learning sub-word level representations in LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5% greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18%.

* Accepted paper at COLING 2016 

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Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach

Mar 22, 2020
Yao Qiang, Xin Li, Dongxiao Zhu

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.

* to appear in the proceedings of IJCNN'20 

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T-BERT -- Model for Sentiment Analysis of Micro-blogs Integrating Topic Model and BERT

Jun 02, 2021
Sarojadevi Palani, Prabhu Rajagopal, Sidharth Pancholi

Sentiment analysis (SA) has become an extensive research area in recent years impacting diverse fields including ecommerce, consumer business, and politics, driven by increasing adoption and usage of social media platforms. It is challenging to extract topics and sentiments from unsupervised short texts emerging in such contexts, as they may contain figurative words, strident data, and co-existence of many possible meanings for a single word or phrase, all contributing to obtaining incorrect topics. Most prior research is based on a specific theme/rhetoric/focused-content on a clean dataset. In the work reported here, the effectiveness of BERT(Bidirectional Encoder Representations from Transformers) in sentiment classification tasks from a raw live dataset taken from a popular microblogging platform is demonstrated. A novel T-BERT framework is proposed to show the enhanced performance obtainable by combining latent topics with contextual BERT embeddings. Numerical experiments were conducted on an ensemble with about 42000 datasets using NimbleBox.ai platform with a hardware configuration consisting of Nvidia Tesla K80(CUDA), 4 core CPU, 15GB RAM running on an isolated Google Cloud Platform instance. The empirical results show that the model improves in performance while adding topics to BERT and an accuracy rate of 90.81% on sentiment classification using BERT with the proposed approach.


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Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection

Jul 22, 2019
David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences. They can also be used to generate fake reviews, which can then be used to attack online review systems and influence the buying decisions of online shoppers. A problem in fake review generation is how to generate the desired sentiment/topic. Existing solutions first generate an initial review based on some keywords and then modify some of the words in the initial review so that the review has the desired sentiment/topic. We overcome this problem by using the GPT-2 NLM to generate a large number of high-quality reviews based on a review with the desired sentiment and then using a BERT based text classifier (with accuracy of 96\%) to filter out reviews with undesired sentiments. Because none of the words in the review are modified, fluent samples like the training data can be generated from the learned distribution. A subjective evaluation with 80 participants demonstrated that this simple method can produce reviews that are as fluent as those written by people. It also showed that the participants tended to distinguish fake reviews randomly. Two countermeasures, GROVER and GLTR, were found to be able to accurately detect fake review.

* Submitted to the IEEE International Workshop on Information Forensics and Security (WIFS) 

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OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features

Jun 08, 2019
Wyverson B. de Oliveira, Leyza B. Dorini, Rodrigo Minetto, Thiago H. Silva

Opinion mining in outdoor images posted by users during day-to-day or leisure activities, for example, can provide valuable information to better understand urban areas. In this work, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures, namely, VGG-16, Resnet50, and InceptionV3, as well as one specifically designed for sentiment analysis. The combination of such classifiers, a strategy known as ensemble, is also considered. We also use different experimental setups to evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification performance. The evaluation explores a novel dataset, namely OutdoorSent, of geolocalized urban outdoor images extracted from Instagram related to three sentiment polarities (positive, negative, and neutral), as well as another dataset publicly available (DeepSent). We observe that the incorporation of knowledge related to semantics features tend to improve the accuracy of low-complex ConvNet architectures. Furthermore, we also demonstrated the applicability of our results in the city of Chicago, United States, showing that they can help to understand the subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment. The ConvNet architectures, trained models, and the proposed outdoor image dataset will be publicly available at http://dainf.ct.utfpr.edu.br/outdoorsent.


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Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources

Nov 17, 2020
Sicheng Zhao, Yang Xiao, Jiang Guo, Xiangyu Yue, Jufeng Yang, Ravi Krishna, Pengfei Xu, Kurt Keutzer

Sentiment analysis of user-generated reviews or comments on products and services on social media can help enterprises to analyze the feedback from customers and take corresponding actions for improvement. To mitigate large-scale annotations, domain adaptation (DA) provides an alternate solution by learning a transferable model from another labeled source domain. Since the labeled data may be from multiple sources, multi-source domain adaptation (MDA) would be more practical to exploit the complementary information from different domains. Existing MDA methods might fail to extract some discriminative features in the target domain that are related to sentiment, neglect the correlations of different sources as well as the distribution difference among different sub-domains even in the same source, and cannot reflect the varying optimal weighting during different training stages. In this paper, we propose an instance-level multi-source domain adaptation framework, named curriculum cycle-consistent generative adversarial network (C-CycleGAN). Specifically, C-CycleGAN consists of three components: (1) pre-trained text encoder which encodes textual input from different domains into a continuous representation space, (2) intermediate domain generator with curriculum instance-level adaptation which bridges the gap across source and target domains, and (3) task classifier trained on the intermediate domain for final sentiment classification. C-CycleGAN transfers source samples at an instance-level to an intermediate domain that is closer to target domain with sentiment semantics preserved and without losing discriminative features. Further, our dynamic instance-level weighting mechanisms can assign the optimal weights to different source samples in each training stage. We conduct extensive experiments on three benchmark datasets and achieve substantial gains over state-of-the-art approaches.


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OutdoorSent: Can Semantic Features Help Deep Learning in Sentiment Analysis of Outdoor Images?

Jun 05, 2019
Wyverson B. de Oliveira, Leyza B. Dorini, Rodrigo Minetto, Thiago H. Silva

Opinion mining in outdoor images posted by users during day-to-day or leisure activities, for example, can provide valuable information to better understand urban areas. In this work, we propose a framework to classify the sentiment of outdoor images shared by users on social networks. We compare the performance of state-of-the-art ConvNet architectures, namely, VGG-16, Resnet50, and InceptionV3, as well as one specifically designed for sentiment analysis. The combination of such classifiers, a strategy known as ensemble, is also considered. We also use different experimental setups to evaluate how the merging of deep features and semantic information derived from the scene attributes can improve classification performance. The evaluation explores a novel dataset, namely OutdoorSent, of geolocalized urban outdoor images extracted from Instagram related to three sentiment polarities (positive, negative, and neutral), as well as another dataset publicly available (DeepSent). We observe that the incorporation of knowledge related to semantics features tend to improve the accuracy of low-complex ConvNet architectures. Furthermore, we also demonstrated the applicability of our results in the city of Chicago, United States, showing that they can help to understand the subjective characteristics of different areas of the city. For instance, particular areas of the city tend to concentrate more images of a specific class of sentiment. The ConvNet architectures, trained models, and the proposed outdoor image dataset will be publicly available at http://dainf.ct.utfpr.edu.br/outdoorsent.


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