Abstract

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.

Overview

As a slight warming up exercise, we decided to participate at DeFT's twitter sentiment analysis task. We participated in three tasks, namely:

• task 1: valence classification - that consisted of labeling tweets in three classes: positive, negative and neutral/mixed
• task 2.1: generic class of information - that consisted of labeling tweets in four classes: information, opinion, sentiment, emotion
• task 2.2: detection of specific opinion/sentiment/emotion - that consisted of labeling tweets 18 classes (in French: surprise_negative, accord, tristesse, valorisation, insatisfaction, peur, apaisement, colere, satisfaction, desaccord, deplaisir, derangement, amour, mepris, plaisir, surprise_positive, ennui, devalorisation)

We all used different methods and approaches:

• Christian used boosting decision trees and his well polished tool bonzaiboost
• Vincent used a naive Bayes approach and Okapi
• I used a 1D convolutional neural network

The results were as follows:

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