In this work, we present the participation of IRISA Linkmedia team at DeFT 2015. The team participated in two tasks: i) valence classification of tweets and ii) fine-grained classification of tweets (which includes two sub-tasks: detection of the generic class of the information expressed in a tweet and detection of the specific class of the opinion / sentiment / emotion. For all three problems, we adopt a standard machine learning framework. More precisely, three main methods are proposed and their feasibility for the tasks is analyzed: i) decision trees with boosting (bonzaiboost), ii) Naive Bayes with Okapi and iii) Convolutional Neural Networks (CNNs). Our approaches are voluntarily knowledge free and text-based only, we do not exploit external resources (lexicons, corpora) or tweet metadata. It allows us to evaluate the interest of each method and of traditional bag-of-words representations vs. word embeddings.
As a slight warming up exercise, we decided to participate at DeFT's twitter sentiment analysis task. We participated in three tasks, namely:
We all used different methods and approaches:
The results were as follows:
|CNN + word2vec||0.6580527369||0.6531518201|
|CNN + word2vec||0.5020312287||0.57147084937|
|CNN + word2vec||0.3159632639||0.5525349008|