Acquiring Knowledge of Affective Events from Blogs Using Label Propagation

AAAI Conferences

Many common events in our daily life affect us in positive and negative ways. For example, going on vacation is typically an enjoyable event, while being rushed to the hospital is an undesirable event. In narrative stories and personal conversations, recognizing that some events have a strong affective polarity is essential to understand the discourse and the emotional states of the affected people. However, current NLP systems mainly depend on sentiment analysis tools, which fail to recognize many events that are implicitly affective based on human knowledge about the event itself and cultural norms. Our goal is to automatically acquire knowledge of stereotypically positive and negative events from personal blogs. Our research creates an event context graph from a large collection of blog posts and uses a sentiment classifier and semi-supervised label propagation algorithm to discover affective events. We explore several graph configurations that propagate affective polarity across edges using local context, discourse proximity, and event-event co-occurrence. We then harvest highly affective events from the graph and evaluate the agreement of the polarities with human judgements.

Sentiment Analysis Using Dependency Trees and Named-Entities

AAAI Conferences

There is an increasing interest for valence and emotion sensing using a variety of signals. Text, as a communication channel, gathers a substantial amount of interest for recognizing its underlying sentiment (valence or polarity), affect or emotion (e.g. happy, sadness). We consider recognizing the valence of a sentence as a prior task to emotion sensing. In this article, we discuss our approach to classify sentences in terms of emotional valence. Our supervised system performs syntactic and semantic analysis for feature extraction. It processes the interactions between words in sentences by using dependency parse trees, and it can decide the current polarity of named-entities based on on-the-fly topic modeling. We compared 3 rule-based approaches and two supervised approaches (i.e. Naive Bayes and Maximum Entropy). We trained and tested our system using the SemEval-2007 affective text dataset, which contains news headlines extracted from news websites. Our results show that our systems outperform the systems demonstrated in SemEval-2007.

Sentiment Classification Using the Meaning of Words

AAAI Conferences

Sentiment Classification (SC) is about assigning a positive, negative or neutral label to a piece of text based on its overall opinion. This paper describes our in-progress work on extracting the meaning of words for SC. In particular, we investigate the utility of sense-level polarity information for SC. We first show that methods based on common classification features are not robust and their performance varies widely across different domains. We then show that sense-level polarity information features can significantly improve the performance of SC. We use datasets in different domains to study the robustness of the designated features. Our preliminary results show that the most common sense of the words result in the most robust results across different domains. In addition our observation shows that the sense-level polarity information is useful for producing a set of high-quality seed words which can be used for further improvement of SC task.

SenticNet 2: A Semantic and Affective Resource for Opinion Mining and Sentiment Analysis

AAAI Conferences

Web 2.0 has changed the ways people communicate, collaborate, and express their opinions and sentiments. But despite social data on the Web being perfectly suitable for human consumption, they remain hardly accessible to machines. To bridge the cognitive and affective gap between word-level natural language data and the concept-level sentiments conveyed by them, we developed SenticNet 2, a publicly available semantic and affective resource for opinion mining and sentiment analysis. SenticNet 2 is built by means of sentic computing, a new paradigm that exploits both AI and Semantic Web techniques to better recognize, interpret, and process natural language opinions. By providing the semantics and sentics (that is, the cognitive and affective information) associated with over 14,000 concepts, SenticNet 2 represents one of the most comprehensive semantic resources for the development of affect-sensitive applications in fields such as social data mining, multimodal affective HCI, and social media marketing.

A Simple Approach to Multilingual Polarity Classification in Twitter Machine Learning

Recently, sentiment analysis has received a lot of attention due to the interest in mining opinions of social media users. Sentiment analysis consists in determining the polarity of a given text, i.e., its degree of positiveness or negativeness. Traditionally, Sentiment Analysis algorithms have been tailored to a specific language given the complexity of having a number of lexical variations and errors introduced by the people generating content. In this contribution, our aim is to provide a simple to implement and easy to use multilingual framework, that can serve as a baseline for sentiment analysis contests, and as starting point to build new sentiment analysis systems. We compare our approach in eight different languages, three of them have important international contests, namely, SemEval (English), TASS (Spanish), and SENTIPOLC (Italian). Within the competitions our approach reaches from medium to high positions in the rankings; whereas in the remaining languages our approach outperforms the reported results.