[LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter

Abstract This paper presents the contribution of our team at task 2 of SemEval 2013: Sentiment Analysis in Twitter. We submitted a constrained run for each of the two subtasks. In the Contextual Polarity Disambiguation subtask, we use a sentiment lexicon approach combined with polarity shift detection and tree kernel based classifiers. In the Message Polarity Classification subtask, we focus on the influence of domain information on sentiment classification

JOINT_FORCES : unite competing sentiment classifiers with random forest

In this paper, we describe how we created a meta-classifier to detect the message-level sentiment of tweets. We participated in SemEval-2014 Task 9B by combining the results of several existing classifiers using a random forest. The results of 5 other teams from the competition as well as from 7 general purpose commercial classifiers were used to train the algorithm. This way, we were able to get a boost of up to 3.24 F1 score points

Web Service SWePT: A Hybrid Opinion Mining Approach

[EN] The increasing use of social networks and online sites where people can express their opinions has created a growing interest in Opinion Mining. One of the main tasks of Opinion Mining is to determine whether an opinion is positive or negative. Therefore, the role of the feelings expressed on the web has become crucial, mainly due to the concern of businesses and government to automatically identify the semantic orientation of the views of customers or citizens. This is also a concern, in the area of health to identify psychological disorders. This research focuses on the development of a web application called SWePT (Web Service for Polarity detection in Spanish Texts), which implements the Sequential Minimal Optimization (SMO) algorithm, extracting its features from an affective lexicon in Mexican Spanish. For this purpose, a corpus and an affective lexicon in Mexican Spanish were created. The experiments using three (positive, neutral, negative) and five categories (very positive, positive, neutral, negative, and very negative) allow us to demonstrate the effectiveness of the presented method. SWePT has also been implemented in the Emotion-bracelet interface, which shows the opinion of a user graphically.This work has been partially supported by the Sectorial Fund CONACyT-INEGI: Project with ref. 208471, INFOTEC, Mexico. And, also by the project CNDT-PYR2015-0016, CENIDET, Mexico. The work of the third author was in the framework of the SomEMBED MINECO TIN2015-71147-C2-1-P research project. The National Council for Science and Technology (CONACyT Mexico) has funded the research work of Delia Irazu Hernandez Farias (Grant No. 218109/313683 CVU-369616).Baca-Gomez, YR.; Martínez, A.; Rosso, P.; Estrada Esquivel, H.; Hernandez-Farias, DI. (2016). Web Service SWePT: A Hybrid Opinion Mining Approach. Journal of Universal Computer Science. 22(5):671-690. https://doi.org/10.3217/jucs-022-05-067167169022

The Emotional Impact of Audio - Visual Stimuli

Induced affect is the emotional effect of an object on an individual. It can be quantified through two metrics: valence and arousal. Valance quantifies how positive or negative something is, while arousal quantifies the intensity from calm to exciting. These metrics enable researchers to study how people opine on various topics. Affective content analysis of visual media is a challenging problem due to differences in perceived reactions. Industry standard machine learning classifiers such as Support Vector Machines can be used to help determine user affect. The best affect-annotated video datasets are often analyzed by feeding large amounts of visual and audio features through machine-learning algorithms. The goal is to maximize accuracy, with the hope that each feature will bring useful information to the table. We depart from this approach to quantify how different modalities such as visual, audio, and text description information can aid in the understanding affect. To that end, we train independent models for visual, audio and text description. Each are convolutional neural networks paired with support vector machines to classify valence and arousal. We also train various ensemble models that combine multi-modal information with the hope that the information from independent modalities benefits each other. We find that our visual network alone achieves state-of-the-art valence classification accuracy and that our audio network, when paired with our visual, achieves competitive results on arousal classification. Each network is much stronger on one metric than the other. This may lead to more sophisticated multimodal approaches to accurately identifying affect in video data. This work also contributes to induced emotion classification by augmenting existing sizable media datasets and providing a robust framework for classifying the same

Die Verwendung von Emojis in der Konsumentenkommunikation – Eine stimmungsanalytische Betrachtung von Kurznachrichten im Social Web

Social media platforms as enabler for real time and many-to-many communication play an important role in the analysis of consumers’ opinions, attitudes, moods, and behaviors towards brands. Emojis as a non-verbal, explanatory and emotional component are increasingly used for a more expressive online communication. While current emotion mining tools only focus on text analysis, we are the first who conduct an automated sentiment analysis of brand-related tweets containing emojis in addition to text. Within the scope of our master thesis at the marketing faculty at University Duisburg-Essen we analyzed 999,197 Starbucks-related and 566,597 McDonald’s-related tweets. We used tweets directed at two different global brands in the fast food sector to increase generalizability. On a sentiment polarity scale, the analyzed tweets show a rather positive sentiment value towards Starbucks and a slightly negative sentiment value towards McDonald’s. We also find that sentiment is classified identically across brands for 94% of emojis. We conclude that the sentiment value can be considered as an indicator for the perceived image of a brand. Our approach provides an innovative tool for companies to directly analyze emotional content on social media platforms and improves the understanding for the needs of consumers.Keywords: Sentiment analysis, Emoji, Twitter, brand, Stimmungsanalys

Exploiting domain knowledge to enhance opinion mining using a hybrid semantic knowledgebase-machine learning approach

With the fast growth of World Wide Web 2.0, a great number of opinions about a variety of products have been published on blogs, forums, and social networks. Online opinions play an important role in supporting consumers make decisions about purchasing products or services. In addition, customer reviews allow companies to understand the strengths and limitations of their products and services, which aids in improving their marketing campaigns. The challenge is that online opinions are predominantly expressed in natural language text, and hence opinion mining tools are required to facilitate the effective analysis of opinions from the unstructured text and to allow for qualitative information extraction. This research presents a Hybrid Semantic Knowledgebase-Machine Learning approach for mining opinions at the domain feature level and classifying the overall opinion on a multi-point scale. The proposed approach benefits from the advantages of deploying a novel Semantic Knowledgebase approach to analyse a collection of reviews at the domain feature level and produce a set of structured information that associates the expressed opinions with specific domain features. The information in the knowledgebase is further supplemented with domain-relevant facts sourced from public Semantic datasets, and the enriched semantically-tagged information is then used to infer valuable semantic information about the domain as well as the expressed opinions on the domain features by summarising the overall opinions about the domain across multiple reviews, and by averaging the overall opinions about other cinematic features. The retrieved semantic information represents a valuable resource for training a Machine Learning classifier to predict the numerical rating of each review. Experimental evaluation revealed that the proposed Hybrid Semantic Knowledgebase-Machine Learning approach improved the precision and recall of the extracted domain features, and hence proved suitable for producing an enriched dataset of semantic features that resulted in higher classification accuracy