Multi-channel convolutional neural network for targeted sentiment classification

In recent years, targeted sentiment analysis has received great attention as a fine-grained sentiment analysis. Determining the sentiment polarity of a specific target in a sentence is the main task. This paper proposes a multi-channel convolutional neural network (MCL-CNN) for targeted sentiment classification. Our approach can not only parallelize over the words of a sentence, but also extract local features effectively. Contexts and targets can be more comprehensively utilized by using part-of-speech information, semantic information and interactive information, so that diverse features can be obtained. Finally, experimental results on the SemEval 2014 dataset demonstrate the effectiveness of this method

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis of Short and Informal Messages on Social Media

Sentiment analysis has been emerging recently as one of the major natural language processing (NLP) tasks in many applications. Especially, as social media channels (e.g. social networks or forums) have become significant sources for brands to observe user opinions about their products, this task is thus increasingly crucial. However, when applied with real data obtained from social media, we notice that there is a high volume of short and informal messages posted by users on those channels. This kind of data makes the existing works suffer from many difficulties to handle, especially ones using deep learning approaches. In this paper, we propose an approach to handle this problem. This work is extended from our previous work, in which we proposed to combine the typical deep learning technique of Convolutional Neural Networks with domain knowledge. The combination is used for acquiring additional training data augmentation and a more reasonable loss function. In this work, we further improve our architecture by various substantial enhancements, including negation-based data augmentation, transfer learning for word embeddings, the combination of word-level embeddings and character-level embeddings, and using multitask learning technique for attaching domain knowledge rules in the learning process. Those enhancements, specifically aiming to handle short and informal messages, help us to enjoy significant improvement in performance once experimenting on real datasets.Comment: A Preprint of an article accepted for publication by Inderscience in IJCVR on September 201

The Many Moods of Emotion

This paper presents a novel approach to the facial expression generation problem. Building upon the assumption of the psychological community that emotion is intrinsically continuous, we first design our own continuous emotion representation with a 3-dimensional latent space issued from a neural network trained on discrete emotion classification. The so-obtained representation can be used to annotate large in the wild datasets and later used to trained a Generative Adversarial Network. We first show that our model is able to map back to discrete emotion classes with a objectively and subjectively better quality of the images than usual discrete approaches. But also that we are able to pave the larger space of possible facial expressions, generating the many moods of emotion. Moreover, two axis in this space may be found to generate similar expression changes as in traditional continuous representations such as arousal-valence. Finally we show from visual interpretation, that the third remaining dimension is highly related to the well-known dominance dimension from psychology

Sentiment Analysis of Afaan Oromoo Facebook Media Using Deep Learning Approach

The rapid development and popularity of social media and social networks provide people with unprecedented opportunities to express and share their thoughts, views, opinions and feelings about almost anything through their personal webpages and blogs or using social network sites like Facebook, Twitter, and Blogger.  This study focuses on sentiment analysis of social media content because automatically identifying and classifying opinions from social media posts can provide significant economic values and social benefits. The major problem with sentiment analysis of social media posts is that it is extremely vast, fragmented, unorganized and unstructured. Nevertheless, many organizations and individuals are highly interested to know what other peoples are thinking or feeling about their services and products. Therefore, sentiment analysis has increasingly become a major area of research interest in the field of Natural Language Processing and Text Mining. In general, sentiment analysis is the process of automatically identifying and categorizing opinions in order to determine whether the writer's attitude towards a particular entity is positive or negative. To the best of the researcher’s knowledge, there is no Deep learning approach done for Afaan Oromoo Sentiment analysis to identify the opinion of the people on social media content. Therefore, in this study, we focused on investigating Convolutional Neural Network and Long Short Term Memory deep learning approaches for the development of sentiment analysis of Afaan Oromoo social media content such as Facebook posts comments. To this end, a total of 1452 comments collected from the official site of the Facebook page of Oromo Democratic Party/ODP for the study. After collecting the data, manual annotation is undertaken. Preprocessing, normalization, tokenization, stop word removal of the sentence are performed. We used the Keras deep learning python library to implement both deep learning algorithms. Long Short Term Memory and Convolutional Neural Network, we used word embedding as a feature. We conducted our experiment on the selected classifiers. For classifiers, we used 80% training and 20% testing rule. According to the experiment, the result shows that Convolutional Neural Network achieves the accuracy of 89%. The Long Short Memory achieves accuracy of 87.6%. Even though the result is promising there are still challenges. Keywords: Sentiment Analysis; Opinionated Afaan Oromoo facebook comments; Oromo Democratic Party Facebook page DOI: 10.7176/NMMC/90-02 Publication date:May 31st 202

LIA@CLEF 2018: Mining events opinion argumentation from raw unlabeled Twitter data using convolutional neural network

International audienceSocial networks on the Internet are becoming increasingly important in our society. In recent years, this type of media, through communication platforms such as Twitter, has brought new research issues due to the massive size of data exchanged and the important number of ever-increasing users. In this context, the CLEF 2018 Mining opinion argumentation task aims to retrieve, for a specific event (festival name or topic), the most diverse argumentative microblogs from a large collection of tweets about festivals in different languages. In this paper, we propose a four-step approach for extracting argumentative microblogs related to a specific query (or event) while no reference data is provided

A Multi-modal Approach to Fine-grained Opinion Mining on Video Reviews

Despite the recent advances in opinion mining for written reviews, few works have tackled the problem on other sources of reviews. In light of this issue, we propose a multi-modal approach for mining fine-grained opinions from video reviews that is able to determine the aspects of the item under review that are being discussed and the sentiment orientation towards them. Our approach works at the sentence level without the need for time annotations and uses features derived from the audio, video and language transcriptions of its contents. We evaluate our approach on two datasets and show that leveraging the video and audio modalities consistently provides increased performance over text-only baselines, providing evidence these extra modalities are key in better understanding video reviews.Comment: Second Grand Challenge and Workshop on Multimodal Language ACL 202