A Deep Learning Architecture for Sentiment Analysis

The fabulous results of Deep Convolution Neural Networks in computer vision and image analysis have recently attracted considerable attention from researchers of other application domains as well. In this paper we present NgramCNN, a neural network architecture we designed for sentiment analysis of long text documents. It uses pretrained word embeddings for dense feature representation and a very simple single-layer classifier. The complexity is encapsulated in feature extraction and selection parts that benefit from the effectiveness of convolution and pooling layers. For evaluation we utilized different kinds of emotional text datasets and achieved an accuracy of 91.2 % accuracy on the popular IMDB movie reviews. NgramCNN is more accurate than similar shallow convolution networks or deeper recurrent networks that were used as baselines. In the future, we intent to generalize the architecture for state of the art results in sentiment analysis of variable-length texts

Modeling Long-Range Context for Concurrent Dialogue Acts Recognition

In dialogues, an utterance is a chain of consecutive sentences produced by one speaker which ranges from a short sentence to a thousand-word post. When studying dialogues at the utterance level, it is not uncommon that an utterance would serve multiple functions. For instance, "Thank you. It works great." expresses both gratitude and positive feedback in the same utterance. Multiple dialogue acts (DA) for one utterance breeds complex dependencies across dialogue turns. Therefore, DA recognition challenges a model's predictive power over long utterances and complex DA context. We term this problem Concurrent Dialogue Acts (CDA) recognition. Previous work on DA recognition either assumes one DA per utterance or fails to realize the sequential nature of dialogues. In this paper, we present an adapted Convolutional Recurrent Neural Network (CRNN) which models the interactions between utterances of long-range context. Our model significantly outperforms existing work on CDA recognition on a tech forum dataset.Comment: Accepted to CIKM '1

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

[multi’vocal]: reflections on engaging everyday people in the development of a collective non-binary synthesized voice

The growing field of Human-Computer Interaction (HCI) takes a step out from conventional screenbased interactions, creating new scenarios, in which voice synthesis and voice recognition become important elements. Such voices are commonly created through concatenative or parametric synthesis methods, which access large voice corpora, pre-recorded by a single professional voice actor. These designed voices arguably propagate representations of gender binary identities. In this paper we present our project, [multi’vocal], which aims to challenge the current gender binary representations in synthesized voices. More specifically we explore if it is possible to create a non-binary synthesized voice through engaging everyday people of diverse backgrounds in giving voice to a collective synthesized voice of all genders, ages and accents

A Convolutional Neural Network for Modelling Sentences

The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence, capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline

Czech Speech Synthesis with Generative Neural Vocoder

In recent years, new neural architectures for generating high-quality synthetic speech on a per-sample basis were introduced. We describe our application of statistical parametric speech synthesis based on LSTM neural networks combined with a generative neural vocoder for the Czech language. We used a traditional LSTM architecture for generating vocoder parametrization from linguistic features. We replaced a standard vocoder with a WaveRNN neural network. We conducted a MUSHRA listening test to compare the proposed approach with the unit selection and LSTM-based parametric speech synthesis utilizing a standard vocoder. In contrast with our previous work, we managed to outperform a well-tuned unit selection TTS system by a great margin on both professional and amateur voices