In the mood: the dynamics of collective sentiments on Twitter

arXiv.org Machine Learning

We study the relationship between the sentiment levels of Twitter users and the evolving network structure that the users created by @-mentioning each other. We use a large dataset of tweets to which we apply three sentiment scoring algorithms, including the open source SentiStrength program. Specifically we make three contributions. Firstly we find that people who have potentially the largest communication reach (according to a dynamic centrality measure) use sentiment differently than the average user: for example they use positive sentiment more often and negative sentiment less often. Secondly we find that when we follow structurally stable Twitter communities over a period of months, their sentiment levels are also stable, and sudden changes in community sentiment from one day to the next can in most cases be traced to external events affecting the community. Thirdly, based on our findings, we create and calibrate a simple agent-based model that is capable of reproducing measures of emotive response comparable to those obtained from our empirical dataset.


Co-Training Based Bilingual Sentiment Lexicon Learning

AAAI Conferences

In this paper, we address the issue of bilingual sentiment lexicon learning(BSLL) which aims to automatically and simultaneously generate sentiment words for two languages. The underlying motivation is that sentiment information from two languages can perform iterative mutual-teaching in the learning procedure. We propose to develop two classifiers to determine the sentiment polarities of words under a co-training framework, which makes full use of the two-view sentiment information from the two languages. The word alignment derived from the parallel corpus is leveraged to design effective features and to bridge the learning of the two classifiers. The experimental results on English and Chinese languages show the effectiveness of our approach in BSLL.


Opinion Context Extraction for Aspect Sentiment Analysis

AAAI Conferences

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentiment co-occurrence knowledge. Further, we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context is effective in improving classification performance.Specifically combining syntactical features with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance.


Learning Latent Sentiment Scopes for Entity-Level Sentiment Analysis

AAAI Conferences

In this paper, we focus on the task of extracting named entities together with their associated sentiment information in a joint manner. Our key observation in such an entity-level sentiment analysis (a.k.a. targeted sentiment analysis) task is that there exists a sentiment scope within which each named entity is embedded, which largely decides the sentiment information associated with the entity. However, such sentiment scopes are typically not explicitly annotated in the data, and their lengths can be unbounded. Motivated by this, unlike traditional approaches that cast this problem as a simple sequence labeling task, we propose a novel approach that can explicitly model the latent sentiment scopes. Our experiments on the standard datasets demonstrate that our approach is able to achieve better results compared to existing approaches based on conventional conditional random fields (CRFs) and a more recent work based on neural networks.


Sentiment Analysis with Global Topics and Local Dependency

AAAI Conferences

With the development of Web 2.0, sentiment analysis has now become a popular research problem to tackle. Recently, topic models have been introduced for the simultaneous analysis for topics and the sentiment in a document. These studies, which jointly model topic and sentiment, take the advantage of the relationship between topics and sentiment, and are shown to be superior to traditional sentiment analysis tools. However, most of them make the assumption that, given the parameters, the sentiments of the words in the document are all independent. In our observation, in contrast, sentiments are expressed in a coherent way. The local conjunctive words, such as “and” or “but”, are often indicative of sentiment transitions. In this paper, we propose a major departure from the previous approaches by making two linked contributions. First, we assume that the sentiments are related to the topic in the document, and put forward a joint sentiment and topic model, i.e. Sentiment-LDA. Second, we observe that sentiments are dependent on local context. Thus, we further extend the Sentiment-LDA model to Dependency-Sentiment-LDA model by relaxing the sentiment independent assumption in Sentiment-LDA. The sentiments of words are viewed as a Markov chain in Dependency-Sentiment-LDA. Through experiments, we show that exploiting the sentiment dependency is clearly advantageous, and that the Dependency-Sentiment-LDA is an effective approach for sentiment analysis.