Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake -- to our knowledge for the first time -- an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two large-scale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus.
National Institute of Technology（国家理工学院）；Samsung R and D Institute India（三星印度研发研究院）
Sentiment analysis is a domain of study that focuses on identifying and classifying the ideas expressed in the form of text into positive, negative and neutral polarities. Feature selection is a crucial process in machine learning. In this paper, we aim to study the performance of different feature selection techniques for sentiment analysis. Term Frequency Inverse Document Frequency (TF-IDF) is used as the feature extraction technique for creating feature vocabulary. Various Feature Selection (FS) techniques are experimented to select the best set of features from feature vocabulary. The sele…
Sentiment analysis or opinion mining has become an open research domain after proliferation of Internet and Web 2.0 social media. People express their attitudes and opinions on social media including blogs, discussion forums, tweets, etc. and, sentiment analysis concerns about detecting and extracting sentiment or opinion from online text. Sentiment based text classification is different from topical text classification since it involves discrimination based on expressed opinion on a topic. Feature selection is significant for sentiment analysis as the opinionated text may have high dimensions…
University of Zagreb（萨格勒布大学）；Tilde（蒂尔德公司）；University of Latvia（拉脱维亚大学）
In this paper, we present various pre-training strategies that aid in im-proving the accuracy of the sentiment classification task. We, at first, pre-trainlanguage representation models using these strategies and then fine-tune them onthe downstream task. Experimental results on a time-balanced tweet evaluation setshow the improvement over the previous technique. We achieve 76% accuracy forsentiment analysis on Latvian tweets, which is a substantial improvement over pre-vious work
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 in…
Indian Institute of Technology Gandhinagar（印度理工学院甘地讷格尔分校）
Code-mixing is the phenomenon of using multiple languages in the same utterance of a text or speech. It is a frequently used pattern of communication on various platforms such as social media sites, online gaming, product reviews, etc. Sentiment analysis of the monolingual text is a well-studied task. Code-mixing adds to the challenge of analyzing the sentiment of the text due to the non-standard writing style. We present a candidate sentence generation and selection based approach on top of the Bi-LSTM based neural classifier to classify the Hinglish code-mixed text into one of the three sent…
Sentiment Analysis and other semantic tasks are commonly used for social media textual analysis to gauge public opinion and make sense from the noise on social media. The language used on social media not only commonly diverges from the formal language, but is compounded by codemixing between languages, especially in large multilingual societies like India. Traditional methods for learning semantic NLP tasks have long relied on end to end task specific training, requiring expensive data creation process, even more so for deep learning methods. This challenge is even more severe for resource sc…
Sharif University of Technology（谢里夫理工大学）；University of Guilan（吉兰大学）
Sentiment analysis refers to the use of natural language processing to identify and extract subjective information from textual resources. One approach for sentiment extraction is using a sentiment lexicon. A sentiment lexicon is a set of words associated with the sentiment orientation that they express. In this paper, we describe the process of generating a general purpose sentiment lexicon for Persian. A new graph-based method is introduced for seed selection and expansion based on an ontology. Sentiment lexicon generation is then mapped to a document classification problem. We used the K-ne…
Nankai University（南开大学）；IBM Research - China（IBM中国研究院）
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…
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis
Bosch Center for Artificial Intelligence（博世人工智能中心）；Center for Information and Language Processing (CIS), LMU Munich（慕尼黑大学语言信息处理中心）；Karlsruhe University of Applied Sciences（卡尔斯鲁厄应用科学大学）
Domain-Adaptive Pre-Training for Arabic Aspect-Based Sentiment Analysis: A Comparative Study of Domain Adaptation and Fine-Tuning Strategies
Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziza University, Jeddah, Saudi Arabia（计算机科学系，计算与信息技术学院，国王阿卜杜勒阿齐兹大学，沙特阿拉伯吉达）
This paper describes our multi-view ensemble approach to SemEval-2017 Task 4 on Sentiment Analysis in Twitter, specifically, the Message Polarity Classification subtask for English (subtask A). Our system is a voting ensemble, where each base classifier is trained in a different feature space. The first space is a bag-of-words model and has a Linear SVM as base classifier. The second and third spaces are two different strategies of combining word embeddings to represent sentences and use a Linear SVM and a Logistic Regressor as base classifiers. The proposed system was ranked 18th out of 38 sy…
City University of Hong Kong（香港城市大学）；USTC-CityU Joint Advanced Research Centre（中国科学技术大学-香港城市大学联合高等研究中心）
With the proliferation of its applications in various industries, sentiment analysis by using publicly available web data has become an active research area in text classification during these years. It is argued by researchers that semi-supervised learning is an effective approach to this problem since it is capable to mitigate the manual labeling effort which is usually expensive and time-consuming. However, there was a long-term debate on the effectiveness of unlabeled data in text classification. This was partially caused by the fact that many assumptions in theoretic analysis often do not…
Fudan University（复旦大学）；IDA Institute Digital Assets, Bucharest University of Economic Studies（布加勒斯特经济研究大学数字资产研究所）；National Yang Ming Chiao Tung University（阳明交通大学）；BRC Blockchain Research Center, Humboldt Universität zu Berlin（柏林洪堡大学区块链研究中心）；Charles University（查理大学）
Indian Institute of Technology (BHU)（印度理工学院（贝拿勒斯印度教大学））
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.
Tennessee State University（田纳西州立大学）；Old Dominion University（欧道明大学）
Processing of raw text is the crucial first step in text classification and sentiment analysis. However, text processing steps are often performed using off-the-shelf routines and pre-built word dictionaries without optimizing for domain, application, and context. This paper investigates the effect of seven text processing scenarios on a particular text domain (Twitter) and application (sentiment classification). Skip gram-based word embeddings are developed to include Twitter colloquial words, emojis, and hashtag keywords that are often removed for being unavailable in conventional literature…
Samsung Research Institute Bangalore（三星研究院班加罗尔分院）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；IITB-Monash Research Academy（印度理工学院孟买分校-莫纳什大学研究院）；Monash University（莫纳什大学）；CSIRO Data61（澳大利亚联邦科学与工业研究组织Data61部门）
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient. However, the decision to choose a domain (known as the source domain) to leverage from is, at best, intuitive. In this paper, we investigate text similarity metrics to facilitate source domain selection for CDSA. We report results on 20 domains (all possible pairs) using 11 similarity metrics. Specifically, we compare CDSA performance with these metrics for different domain-pairs to enable the selection o…
Sentiment analysis is a costly yet necessary task for enterprises to study the opinions of their customers to improve their products and to determine optimal marketing strategies. Due to the existence of a wide range of domains across different products and services, cross-domain sentiment analysis methods have received significant attention. These methods mitigate the domain gap between different applications by training cross-domain generalizable classifiers which help to relax the need for data annotation for each domain. Most existing methods focus on learning domain-agnostic representatio…
This work investigates the application of sampling methods for sentiment analysis on two different highly imbalanced datasets. One dataset contains online user reviews from the cooking platform Epicurious and the other contains comments given to the Planned Parenthood organization. In both these datasets, the classes of interest are rare. Word n-grams were used as features from these datasets. A feature selection technique based on information gain is first applied to reduce the number of features to a manageable space. A number of different sampling methods were then applied to mitigate the c…
Lahore University of Management Sciences (LUMS)（拉合尔管理科学大学）；Islamic University（伊斯兰大学）
Social media platforms and online forums generate rapid and increasing amount of textual data. Businesses, government agencies, and media organizations seek to perform sentiment analysis on this rich text data. The results of these analytics are used for adapting marketing strategies, customizing products, security and various other decision makings. Sentiment analysis has been extensively studied and various methods have been developed for it with great success. These methods, however apply to texts written in a specific language. This limits applicability to a limited demographic and a speci…
Sentiment Analysis in Arabic is a challenging task due to the rich morphology of the language. Moreover, the task is further complicated when applied to Twitter data that is known to be highly informal and noisy. In this paper, we develop a hybrid method for sentiment analysis for Arabic tweets for a specific Arabic dialect which is the Saudi Dialect. Several features were engineered and evaluated using a feature backward selection method. Then a hybrid method that combines a corpus-based and lexicon-based method was developed for several classification models (two-way, three-way, four-way). T…
This paper describes the participation of the team "TwiSE" in the SemEval 2016 challenge. Specifically, we participated in Task 4, namely "Sentiment Analysis in Twitter" for which we implemented sentiment classification systems for subtasks A, B, C and D. Our approach consists of two steps. In the first step, we generate and validate diverse feature sets for twitter sentiment evaluation, inspired by the work of participants of previous editions of such challenges. In the second step, we focus on the optimization of the evaluation measures of the different subtasks. To this end, we examine diff…
The complexities of Arabic language in morphology, orthography and dialects makes sentiment analysis for Arabic more challenging. Also, text feature extraction from short messages like tweets, in order to gauge the sentiment, makes this task even more difficult. In recent years, deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications. Word embedding, or word distributing approach, is a current and powerful tool to capture together the closest words from a contextual text. In this paper, we describe how we co…
Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about peoples sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social …
University of Edinburgh（爱丁堡大学）；Huawei Research Centre（华为研究中心）
Aspect Based Sentiment Analysis is a dominant research area with potential applications in social media analytics, business, finance, and health. Prior works in this area are primarily based on supervised methods, with a few techniques using weak supervision limited to predicting a single aspect category per review sentence. In this paper, we present an extremely weakly supervised multi-label Aspect Category Sentiment Analysis framework which does not use any labelled data. We only rely on a single word per class as an initial indicative information. We further propose an automatic word select…
BERT has revolutionized the NLP field by enabling transfer learning with large language models that can capture complex textual patterns, reaching the state-of-the-art for an expressive number of NLP applications. For text classification tasks, BERT has already been extensively explored. However, aspects like how to better cope with the different embeddings provided by the BERT output layer and the usage of language-specific instead of multilingual models are not well studied in the literature, especially for the Brazilian Portuguese language. The purpose of this article is to conduct an exten…
Robust Multimodal Sentiment Analysis of Image-Text Pairs by Distribution-Based Feature Recovery and Fusion
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；TMCC, College of Computer Science, Nankai University（TMCC，南开大学计算机学院）
Netmarble AI Center（网石AI中心）；Kakao Corp.（Kakao公司）；Wisenut Inc.（Wisenut公司）；Kakao Enterprise（Kakao企业）；Konkuk University（建国大学）
Existing works for aspect-based sentiment analysis (ABSA) have adopted a unified approach, which allows the interactive relations among subtasks. However, we observe that these methods tend to predict polarities based on the literal meaning of aspect and opinion terms and mainly consider relations implicitly among subtasks at the word level. In addition, identifying multiple aspect-opinion pairs with their polarities is much more challenging. Therefore, a comprehensive understanding of contextual information w.r.t. the aspect and opinion are further required in ABSA. In this paper, we propose …
On social media, Arabic people tend to express themselves in their own local dialects. More particularly, Tunisians use the informal way called "Tunisian Arabizi". Analytical studies seek to explore and recognize online opinions aiming to exploit them for planning and prediction purposes such as measuring the customer satisfaction and establishing sales and marketing strategies. However, analytical studies based on Deep Learning are data hungry. On the other hand, African languages and dialects are considered low resource languages. For instance, to the best of our knowledge, no annotated Tuni…
The Chinese University of Hong Kong（香港中文大学）；Alibaba DAMO Academy（阿里巴巴达摩院）
In this paper, we investigate the modeling power of contextualized embeddings from pre-trained language models, e.g. BERT, on the E2E-ABSA task. Specifically, we build a series of simple yet insightful neural baselines to deal with E2E-ABSA. The experimental results show that even with a simple linear classification layer, our BERT-based architecture can outperform state-of-the-art works. Besides, we also standardize the comparative study by consistently utilizing a hold-out validation dataset for model selection, which is largely ignored by previous works. Therefore, our work can serve as a B…
Nanyang Technological University（南洋理工大学）；IBM Research Australia（IBM澳大利亚研究院）
Situated in the global outbreak of COVID-19, our study enriches the discussion concerning the emergent racism and xenophobia on social media. With big data extracted from Twitter, we focus on the analysis of negative sentiment reflected in tweets marked with racist hashtags, as racism and xenophobia are more likely to be delivered via the negative sentiment. Especially, we propose a stage-based approach to capture how the negative sentiment changes along with the three development stages of COVID-19, under which it transformed from a domestic epidemic into an international public health emerge…
Language Technologies Research Center（语言技术研究中心）；Kohli Center on Intelligent Systems（科利智能系统中心）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）
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…
In present century, data volume is increasing enormously. The data could be in form for image, text, voice, and video. One factor in this huge growth of data is usage of social media where everyone is posting data on daily basis during chatting, exchanging information, and uploading their personal and official credential. Research of sentiments seeks to uncover abstract knowledge in Published texts in which users communicate their emotions and thoughts about shared content, including blogs, news and social networks. Roman Urdu is the one of most dominant language on social networks in Pakistan…
LIAAD - INESC TEC；Faculty of Sciences-University of Porto（波尔图大学理学院）；Saarland University（萨尔大学）；Google Research（谷歌研究院）；Bayero University（巴耶罗大学）；Ahmadu Bello University（艾哈迈杜·贝罗大学）；Microsoft Research India（微软研究院印度）；Technical University of Munich（慕尼黑工业大学）；Clear Global；Kaduna state University（卡杜纳州立大学）
Sentiment analysis is one of the most widely studied applications in NLP, but most work focuses on languages with large amounts of data. We introduce the first large-scale human-annotated Twitter sentiment dataset for the four most widely spoken languages in Nigeria (Hausa, Igbo, Nigerian-Pidgin, and Yorùbá ) consisting of around 30,000 annotated tweets per language (and 14,000 for Nigerian-Pidgin), including a significant fraction of code-mixed tweets. We propose text collection, filtering, processing and labeling methods that enable us to create datasets for these low-resource languages. We …
UCL（伦敦大学学院）；London School of Economics and Political Sciences（伦敦政治经济学院）
This paper covers the two approaches for sentiment analysis: i) lexicon based method; ii) machine learning method. We describe several techniques to implement these approaches and discuss how they can be adopted for sentiment classification of Twitter messages. We present a comparative study of different lexicon combinations and show that enhancing sentiment lexicons with emoticons, abbreviations and social-media slang expressions increases the accuracy of lexicon-based classification for Twitter. We discuss the importance of feature generation and feature selection processes for machine learn…
