The Web is evolving through an era where the opinions of users are getting increasingly important and valuable. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for tasks such as social media marketing, branding, product positioning, and corporate reputation management. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions involves a deep understanding of natural language text by machines, from which we are still very far.

Singapore Symposium on Sentiment Analysis (S3A) is a biennial event aiming to bridge such a gap by exploring novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data. S3A aims to provide a national forum for Singapore-based researchers working in the field of sentiment analysis and related topics to share information on their latest investigations and their applications both in academic research areas and industrial sectors.

S3A'15 (6th February 2015, NTU)
S3A'13 (1st November 2013, NTU)

The broader context of the symposium comprehends AI, linguistics, psychology, sociology, and ethics. Topics of interest include but are not limited to:
• Sentiment identification & classification
• Subjectivity detection
• Emotion recognition
Singapore Symposium on Sentiment Analysis • Cultural-dependent sentiment analysis
• Personality detection
• Multi-lingual sentiment analysis
• Multi-modal sentiment analysis
• Multi-domain & cross-domain evaluation
• Opinion and sentiment summarization & visualization
• Big social data analysis
• Social ranking
• Social network analysis
• Social media marketing
• Comparative opinion analysis
• Opinion spam detection

S3A'15 (6th February 2015, NTU)                                                                           GO TO TOP
Location: HSS Conference Room

10.00 – 10.15: Welcoming and introduction by Erik Cambria and Francis Bond

10.15 – 11.00: Amit Sheth (Ohio Center of Excellence in Knowledge-enabled Computing)
Citizen Sensor Data Mining, Social Media Analytics and Applications

With the rapid rise in the popularity of social media (1B+ Facebook users, 200M+ twitter users), and near ubiquitous mobile access (4+ billion actively-used mobile phones), the sharing of observations and opinions has become common-place (500M+ tweets a day). This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries. I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform. I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (

11.00 – 11.45: Tomoko Ohkuma (Fuji-Xerox)
Sentiment Analysis and User Profiling for SNS Text

The NLP team in the Communication Technology Laboratory is working on research and development of information extraction from SNS text. In this presentation, we introduce research activities about sentiment analysis and user profiling for applications like social listening, reputation management, and marketing. Topics that will be presented are 1) a report of SemEval-2014, 2) sentiment analysis using WSD, 3) targeted sentiment using topic modeling, 4) user gender inference using text and image processing. At the end of this presentation, we talk about a new joint research project that just started between NTU and Fuji Xerox in this February.

11.45 – 12.15: Waifong Boh (NTU Nanyang Business School)
A Temporal Study of the Effects of Online Opinions: Information Sources Matter

This study examines when and why online comments from different sources and platforms influence a movie's box office receipts over time. We tracked over 1,500 sources of online expert and consumer reviews for cinematic movies released for an entire year and continuously monitored major social media sites (e.g. Twitter and Plurk) for comments. We text-mined the comments to elucidate the sentiments and analyzed the data. Premised on the argument that greater uncertainty exists at the beginning of a movie's release, we hypothesized and found that expert reviews, and the valence and volume of comments from pull-based platforms like forums have a significant influence on early box office receipts. In contrast, the valence and volume of comments from push-based platforms like microblogs have a significant influence on later box office receipts, as they serve a reminder rather than an informational role with the decreased uncertainty in these later stages. Our research demonstrates that online opinions are not always persuasive and useful, and our findings provide insights into when consumers are likely to pay attention to which types of online opinions.

12.15 – 12.45: Feida Zhu (SMU School of Information Systems)
Social Media Mining and Analysis for Financial Innovation

The recent blossom of social network services has provided everyone with an unprecedented level of ease and fun of sharing information of all sorts. These public social data therefore reveal a surprisingly large amount of information about an individual which is otherwise unavailable. The business, consumer and social insights attainable from this big and dynamic social data are critically important and immensely valuable in a wide range of applications for both private and public sectors. In particular, there has been a growing interest in harnessing social media data for financial innovation. In this talk, we will explore some recent advances along this direction including personal credit scoring, risk management and customer acquisition.

12.45 – 14.00: Lunch break (food kindly provided by NTU SCE's CIR Lab)

14.00 – 14.30: Chris Khoo (NTU Wee Kim Wee School of Communication and Information)
Comparison of Lexical Resources for Sentiment Analysis

This work sets out a detailed comparison of sentiment lexica (General inquirer, MPQA and Hu & Liu) with WKWSCI lexicon. WKWSCI lexicon contains human annotated words with semantic orientation (polarity and strength). The presentation will provide an overview of the coverage of WKWSCI lexicon, overlap and consistency with other lexicons. We also show lexicon performance in product reviews dataset using bag of words approach.

14.30 – 15.00: Elvis Albertus Bin Toni (NTU School of Humanities and Social Sciences)
Linguistic Expression of Emotions in Lamaholot Language

This study observed the syntactical differences across dialects, metaphors, and borrowing from and/or mixing with other language for linguistic expression of emotions in Lamaholot language. It displays several findings that there are two distinctive syntactical features i.e. the existence of pronoun subject in the expression of emotions and the use of single combination of morphemes across three investigated dialects (Nusa Tadon, Lewo Tobi, and Lewolema). That a metaphor is a vehicle for expression of emotion attested in the three dialects. That ‘One-k’/my heart as a feature of expression of emotion in Lamaholot is shared among the dialects. That borrowing from and/or mixing with Bahasa Indonesia when expressing emotion is common.

15.00 – 15.30: Iti Chaturvedi (NTU School of Computer Science)
Deep Recurrent Neural Networks for Sentiment Analysis

The rise in social media such as blogs and networking websites has resulted in a surge of research in sentiment classification, which aims to determine the judgement of a writer with respect to a given topic based on a given textural comment. The objective is to classify the sentiment polarity of a tweet as positive, negative, or neutral. We propose use of a deep neural network to automatically extract sentiment specific word embedding from tweets. To capture loops and higher-order dependencies in a sequence of words we use Gaussian Bayesian networks. Low dimensional statistically significant word-structures called motifs are extracted from a variety of sources of data. The deep neural network is pre-trained with Gaussian motifs from different classes to reconstruct higher dimensions. The algorithm is validated on document and sentiment analysis benchmarks. Our method is able to outperform baselines in accuracy and is computationally faster.

15.30 – 16.00: Francis Bond (NTU School of Humanities and Social Sciences)
Multi-Lingual Semantic Processing

With physical barriers to information access decreasing, lack of understanding become the greatest impediment to communication. Research on deep linguistic analysis allows us to abstract away from language particular syntactic phenomena to a uniform panlingual semantic representation. By linking this to the wordnet, we can take advantage of a wide variety of linked open data, including sentiment and apply it to hundreds of languages.

16.00 – 16.30: Erik Cambria (NTU School of Computer Engineering)
Sentic Patterns

Sentic patterns merge linguistics, common-sense computing, and machine learning for improving the accuracy of sentiment-analysis tasks such as polarity detection. Sentic patterns allow sentiments to flow from concept to concept based on the dependency relation of the input sentence, like in an electronic circuit where sentiment words are sources while other words are elements, e.g., VERY is an amplifier, NOT is a logical complement, RATHER is a resistor, BUT is an OR-like element that gives preference to one of its inputs. This way, sentic patterns achieve a better understanding of the contextual role of each concept within the sentence and, hence, obtain a polarity detection accuracy that outperforms state-of-the-art statistical methods.

16.30 – 17.00: Final remarks and conclusion by Erik Cambria and Francis Bond

S3A'13 (1st November 2013, NTU)                                                                           GO TO TOP

Location: HSS Seminar Room 3

13.00 – 13.10: Welcoming and introduction

13.10 – 13.30: Grégoire Winterstein (Hong Kong Institute of Education)
Argumentative Operators and Sentiment Analysis

I will provide a brief characterization of the notion of argumentation as it is understood in psychology and linguistics. I will then proceed to show how some linguistic items can best be described in argumentative terms. I will focus on the contributions of 'only', and 'almost'. In a second part I will underline the possible uses of argumentative theories for sentiment analysis and the insights argumentative theories can gather from the output of sentiment analysis models.

13.30 – 13.50: Hai Zhen (NTU School of Computer Science)
Product Review Mining

My talk will focus on product review mining, as briefly summarized below 1. Introduction to review mining (opinion mining, sentiment analysis): background, motivation, introduction 2. Review mining at document (review), sentnece, or phrase level 3. Feature-level review mining 3.1 feature extraction 3.1.1 explicit feature 3.1.2 implicit feature 3.2 opinion word identification and sentiment polarity classification 3.3 summarization 4. Aspect-based review mining (mainly discuss Topic Models) 4.1 aspect detection 4.1 sentiment prediction 5. review helpfulness prediction and review selection 6. Experiments 7. Conclusion

13.50 – 14.10: Lin Qiu (NTU School of Humanities and Social Sciences)
Personality Analysis over Twitter

Microblogging services such as Twitter have become increasingly popular in recent years. However, little is known about how personality is manifested and perceived in microblogs. In this study, we measured the Big Five personality traits of 142 participants and collected their tweets over a 1-month period. Extraversion, agreeableness, openness, and neuroticism were associated with specific linguistic markers, suggesting that personality manifests in microblogs. Meanwhile, eight observers rated the participants’ personality on the basis of their tweets. Results showed that observers relied on specific linguistic cues when making judgments, and could only judge agreeableness and neuroticism accurately. This study provides new empirical evidence of personality expression in naturalistic settings, and points to the potential of utilizing social media for personality research.

14.10 – 14.30: Chris Khoo (NTU Wee Kim Wee School of Communication and Information)
Sentiment Analysis of Movie Reviews, Drug Reviews and Political News

The talk summarizes 3 studies on the sentiment analysis of movie reviews, drug reviews and political news. The first study analysed the differences in sentiment expressions used in movie reviews from four Web genres—blog postings, discussion board threads, user reviews, and reviews by movie critics. Sentiment analysis of movie reviews was performed at the clause level to identify the sentiment orientation and strength towards different aspects of a movie. A method was developed to compute the overall sentiment of a clause based on the sentiment scores of individual words, taken from sentiment lexicons. A visual interface was developed to explore the extracted sentiments. More recently, a similar sentiment analysis approach was applied to drug reviews. The third study was a case study of applying the Appraisal Theory developed by linguists to analyze political news articles.

14.30 – 14.50: Coffee break

14.50 – 15.10: Bai Lin (NTU School of Humanities and Social Sciences)
Communicating Emotions across Cultures

In our increasingly interconnected world, how to communicate across different cultures has become more critical. However, successful communications are always hindered by differences between languages and cultures, and such difficulties become even more obvious when it comes to more personal and emotional topics. How do people from diverse culture backgrounds communicate their emotions? In what ways does an expression of emotion vary across culture? How bilinguals meet with the challenges of cultural or linguistic specificities of their two languages? Can these cultural knowledge of emotion expression be taught? These are the questions that have led me to my PhD. During my Phd project, I try to explore for answers to some of these questions by specifically looking at: 1. How do Chinese and English differ in expressing shame? 2. How Chinese English bilinguals deal with the cultural/ linguistic (Early simultaneous bilinguals in Singapore) 3. How to help late bilinguals overcome the semantic handicap in emotion expression in their L2? I will be sharing some of the preliminary results, and invaluable comments and thoughts will be very much appreciated.

15.10 – 15.30: Guang-Bin Huang (NTU School of Electrical & Electronic Engineering)
Representational Learning with Extreme Learning Machine for Big Data

Neural networks (NN) and support vector machines (SVM) play key roles in machine learning and data analysis in the past 2-3 decades. However, it is known that these popular learning techniques face some challenging issues such as: intensive human intervene, slow learning speed, poor learning scalability. Extreme Learning Machines (ELM) not only learn up to tens of thousands faster than NN and SVMs, but also provide unified implementation for regression, binary and multi-class applications. This talk will give a brief introduction to ELM history and some of its successful applications. This talk will further address three issues: i) why NN and SVM/LS-SVM may only produce suboptimal solutions to ELM; ii) why ELM may outperform Deep Learning in both learning accuracy and learning speed; and iii) why ELM could be a biological inspired learning technique and why ELM is closer to animal brains. Finally, this talk will introduce ELM based Auto Encoder (ELM-AE), which learns feature representations using singular values and can be used as the basic building block for multi-layer ELMs. The resultant multi-layer ELMs achieves higher learning accuracy than auto encoders based deep networks, Deep Belief Networks (DBN), Deep Boltzmann Machines (DBM) for MNIST dataset, and is thousands times faster than any state−of−the−art deep networks.

15.30 – 15.50: Francis Bond (NTU School of Humanities and Social Sciences)
Uniform Cross-lingual Sentiment analysis with Wordnets

By linking semantic representations to a shared multi-lingual semantic network, we can efficiently share information on meaning, including sentiment, across languages.

15.50 – 16.10: Erik Cambria (NUS Temasek Labs)
Jumping NLP Curves

In a Web where user-generated content has already hit critical mass, the need for automated systems for filtering out noise and aggregating meaningful information is growing exponentially. The democratization of online content creation has, in fact, led to the increase of Web debris, which is inevitably and negatively affecting information retrieval, aggregation, and processing. In order to optimize the execution of such tasks, machine-learning techniques have gained high classification efficiency in recent years, and with great success too. However, some of the most effective machine- learning algorithms produce no human understandable results; although they may achieve improved accuracy, little about how and why is known, apart from some superficial knowledge gained in the manual feature engineering process. We are facing a NLP crisis caused by the fact that machine- learning techniques cannot go beyond the syntactical structure of text and, hence, lack domain adaptivity and implicit semantic feature inference. Before such techniques reach saturation, NLP researchers need to 'jump the curve' of concept-level text analysis. Despite still being rather limited by the richness of knowledge bases and ontologies, semantic-based approaches are already making inroads into competing with traditional algorithms due to their nature of truly emulating the way the human mind processes natural language.

16.10 – 16.30: Final remarks and conclusion