1,721,127 research outputs found
SWAP at SemEval-2019 Task 3: Emotion detection in conversations through Tweets, CNN and LSTM deep neural networks
No full textEmotion detection from user-generated contents is growing in importance in the area of natural language processing. The approach we proposed for the EmoContext task is based on the combination of a CNN and an LSTM using a concatenation of word embeddings. A stack of convolutional neural networks (CNN) is used for capturing the hierarchical hidden relations among embedding features. Meanwhile, a long short-term memory network (LSTM) is used for capturing information shared among words of the sentence. Each conversation has been formalized as a list of word embeddings, in particular during experimental runs pre-trained Glove and Google word embeddings have been evaluated. Surface lexical features have been also considered, but they have been demonstrated to be not usefully for the classification in this specific task. The final system configuration achieved a micro F1 score of 0.7089. The python code of the system is fully available at https://github.com/marcopoli/EmoContext201
Context-aware graph-based recommendations exploiting Personalized PageRank
Full text linkIn this article we present a context-aware recommendation method that exploits graph-based data models and Personalized PageRank to provide users with recommendations.
In particular, our approach extends the basic graph-based representation that relies on users and items nodes by introducing a third class of nodes, that is to say, context nodes, whose goal is to model the different contextual situations in which an item can be consumed. Given such a data model, we used Personalized PageRank to identify the most suitable recommendations for each user: in a nutshell, our model is based on the intuition that context nodes shall be used to influence random walks, in order to assist the algorithm in identifying the items that are relevant in a particular contextual setting.
In the experimental evaluation we investigated the effectiveness of the approach on three different datasets. The results showed that our context-aware graph-based approach overcame the baselines in most of the experimental settings and obtained the best overall results in cold-start situations, thus confirming the validity of the methodology
An emotion-driven approach for aspect-based opinion mining
No full textThe remarkable ability to understand the opinion of a user about a specific topic of discussion allows intelligent systems to provide more specific and personalized suggestions especially when no other information is available. The strategies for opinion mining, also known as sentiment analysis, are in last years topic of in-depth studies. In this work, we present an approach of text mining for detecting the topic of discussion for textual contents and the emotion that the writer feels while writing it. Conversely to the classic strategies of sentiment analysis, we enrich the standard polarity prediction task with more fine-grained information about user’s emotion. By using this information, the final behavior of the personalized system could be designed by taking into account the view about the topic of the specific user. For performing this task, we adopted a hybrid approach which uses both lexicons and semantic representation of sentences for the operation of aspect classification. Training data for the aspects detection module have been extracted from already categorized last year world news. The emotional labeling approach is, instead, based on the posts left by users on Facebook, which have been annotated using the emoticon encountered. The evaluation has been conducted on a dataset of tweets opportunely collected using hash-tags which refer both to the topic of discussion and the emotional opinion
Word Embedding techniques for Content-based Recommender Systems: An empirical evaluation
No full textThis work presents an empirical comparison among three widespread word embedding techniques as Latent Semantic Indexing, Random Indexing and the more recent Word2Vec. Specifically, we employed these techniques to learn a low-dimensional vector space word representation and we exploited it to represent both items and user profiles in a content-based recommendation scenario. The performance of the techniques has been evaluated against two state-of-the-art datasets, and experimental results provided good in-sights which pave the way to several future directions
Social Tags and Emotions as main Features for the Next Song To Play in Automatic Playlist Continuation
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Virtual Customer Assistants in finance: From state of the art and practices to design guidelines
No full textVirtual Customer Assistants (VCAs) are revolutionizing the way users interact with machines. VCAs allow a far more natural interaction, and are gaining an increasingly large role in customer service. The financial domain is especially susceptible of this change, because customer care is of paramount importance. Furthermore, VCAs have the potential of supporting customers in performing routine operations such as money transfers, or in more complex decision-making operations such as trading stocks, both of which require that VCAs display strong reliability. This survey has a two-fold goal. First, we perform an analysis of the state of the art and practices of VCAs in the financial domain. Second, we provide a sort of toolbox that collects the best practices for designing and developing a VCA in the financial domain, guaranteeing a high-quality user experience
Preference Learning in Recommender Systems
No full textAs proved by the continuous growth of the number of websites which embody recommender systems as a way of personalizing theexperience of users with their content, recommender systems representone of the most popular applications of principles and techniques com-ing from Information Filtering (IF). As IF techniques usually perform aprogressive removal of non-relevant content according to the informationstored in a user profile, recommendation algorithms process informationabout user interests - acquired in an explicit (e.g., letting users expresstheir opinion about items) or implicit (e.g., studying some behavioralfeatures) way - and exploit these data to generate a list of recommendeditems. Although each type of filtering method has its own weaknessesand strengths, preference handling is one of the core issues in the designof every recommender system: since these systems aim to guide users in apersonalized way to interesting or useful objects in a large space of possi-ble options, it is important for them to accurately catch and model userpreferences. The paper provides a general overview of the approaches tolearning preference models in the context of recommender system
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