Attention-based Multi-modal Sentiment Analysis and Emotion Detection in Conversation using RNN

The availability of an enormous quantity of multimodal data and its widespread applications, automatic sentiment analysis and emotion classification in the conversation has become an interesting research topic among the research community. The interlocutor state, context state between the neighboring utterances and multimodal fusion play an important role in multimodal sentiment analysis and emotion detection in conversation. In this article, the recurrent neural network (RNN) based method is developed to capture the interlocutor state and contextual state between the utterances. The pair-wise attention mechanism is used to understand the relationship between the modalities and their importance before fusion. First, two-two combinations of modalities are fused at a time and finally, all the modalities are fused to form the trimodal representation feature vector. The experiments are conducted on three standard datasets such as IEMOCAP, CMU-MOSEI, and CMU-MOSI. The proposed model is evaluated using two metrics such as accuracy and F1-Score and the results demonstrate that the proposed model performs better than the standard baselines

Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually

Convolutional MKL based multimodal emotion recognition and sentiment analysis

Technology has enabled anyone with an Internet connection to easily create and share their ideas, opinions and content with millions of other people around the world. Much of the content being posted and consumed online is multimodal. With billions of phones, tablets and PCs shipping today with built-in cameras and a host of new video-equipped wearables like Google Glass on the horizon, the amount of video on the Internet will only continue to increase. It has become increasingly difficult for researchers to keep up with this deluge of multimodal content, let alone organize or make sense of it. Mining useful knowledge from video is a critical need that will grow exponentially, in pace with the global growth of content. This is particularly important in sentiment analysis, as both service and product reviews are gradually shifting from unimodal to multimodal. We present a novel method to extract features from visual and textual modalities using deep convolutional neural networks. By feeding such features to a multiple kernel learning classifier, we significantly outperform the state of the art of multimodal emotion recognition and sentiment analysis on different datasets

The Pandemic Effect on the Public Perception of Science: Evidence from Documentary Reviews

openThis study aims to investigate the presence of anti-scientific behavior in user-generated reviews of scientific documentaries. The primary objective is to assess whether the COVID-19 pandemic has influenced individuals' anti-scientific tendencies. By analyzing a dataset of user reviews gathered from various online platforms, the study employs natural language techniques to identify patterns and linguistic cues indicative of anti-scientific sentiments. Furthermore, the study incorporates econometric models, specifically fixed effects models. These models allow for the control of individual-specific characteristics and time-related variations, enabling a robust examination of the relationship between the COVID-19 pandemic and anti-scientific behavior in documentary reviews. The findings of this study not only contribute to our understanding of public attitudes towards science but also shed light on the impact of major global events, such as the COVID-19 pandemic, on anti-scientific sentiments.This study aims to investigate the presence of anti-scientific behavior in user-generated reviews of scientific documentaries. The primary objective is to assess whether the COVID-19 pandemic has influenced individuals' anti-scientific tendencies. By analyzing a dataset of user reviews gathered from various online platforms, the study employs natural language techniques to identify patterns and linguistic cues indicative of anti-scientific sentiments. Furthermore, the study incorporates econometric models, specifically fixed effects models. These models allow for the control of individual-specific characteristics and time-related variations, enabling a robust examination of the relationship between the COVID-19 pandemic and anti-scientific behavior in documentary reviews. The findings of this study not only contribute to our understanding of public attitudes towards science but also shed light on the impact of major global events, such as the COVID-19 pandemic, on anti-scientific sentiments

Synthesising prosody with insufficient context

Prosody is a key component in human spoken communication, signalling emotion, attitude, information structure, intention, and other communicative functions through perceived variation in intonation, loudness, timing, and voice quality. However, the prosody in text-to-speech (TTS) systems is often monotonous and adds no additional meaning to the text. Synthesising prosody is difficult for several reasons: I focus on three challenges. First, prosody is embedded in the speech signal, making it hard to model with machine learning. Second, there is no clear orthography for prosody, meaning it is underspecified in the input text and making it difficult to directly control. Third, and most importantly, prosody is determined by the context of a speech act, which TTS systems do not, and will never, have complete access to. Without the context, we cannot say if prosody is appropriate or inappropriate. Context is wide ranging, but state-of-the-art TTS acoustic models only have access to phonetic information and limited structural information. Unfortunately, most context is either difficult, expensive, or impos- sible to collect. Thus, fully specified prosodic context will never exist. Given there is insufficient context, prosody synthesis is a one-to-many generative task: it necessitates the ability to produce multiple renditions. To provide this ability, I propose methods for prosody control in TTS, using either explicit prosody features, such as F0 and duration, or learnt prosody representations disentangled from the acoustics. I demonstrate that without control of the prosodic variability in speech, TTS will produce average prosody—i.e. flat and monotonous prosody. This thesis explores different options for operating these control mechanisms. Random sampling of a learnt distribution of prosody produces more varied and realistic prosody. Alternatively, a human-in-the-loop can operate the control mechanism—using their intuition to choose appropriate prosody. To improve the effectiveness of human-driven control, I design two novel approaches to make control mechanisms more human interpretable. Finally, it is important to take advantage of additional context as it becomes available. I present a novel framework that can incorporate arbitrary additional context, and demonstrate my state-of- the-art context-aware model of prosody using a pre-trained and fine-tuned language model. This thesis demonstrates empirically that appropriate prosody can be synthesised with insufficient context by accounting for unexplained prosodic variation