Understanding emotions is an important aspect of personal development and growth, and as such it is a key tile for the emulation of human intelligence. Besides being important for the advancement of AI, emotion processing is also important for the closely related task of polarity detection. The opportunity to automatically capture the general public's sentiments about social events, political movements, marketing campaigns, and product preferences has raised interest in both the scientific community, for the exciting open challenges, and the business world, for the remarkable fallouts in marketing and financial market prediction. This has led to the emerging fields of affective computing and sentiment analysis, which leverage human-computer interaction, information retrieval, and multimodal signal processing for distilling people's sentiments from the ever-growing amount of online social data.

Affective Computing and Sentiment Analysis

https://dspace.stir.ac.uk/bitstream/1893/25490/1/affective-computing-review.pdf

A review of affective computing

Gilles Fauconnier & Mark Turner, " The way we think: conceptual blending and the mind's hidden complexities"

Multimodal sentiment analysis is a developing area of research, which involves the identification of sentiments in videos. Current research considers utterances as independent entities, i.e., ignores the interdependencies and relations among the utterances of a video. In this paper, we propose a LSTM-based model that enables utterances to capture contextual information from their surroundings in the same video, thus aiding the classification process. Our method shows 5-10% performance improvement over the state of the art and high robustness to generalizability.

/pdf/context-dependent-sentiment-analysis-in-user-generated-38nyocbzu0.pdf

Context-Dependent Sentiment Analysis in User-Generated Videos.

This article presents a deep learning based method for determining the author's personality type from text: given a text, the presence or absence of the Big Five traits is detected in the author's psychological profile. For each of the five traits, the authors train a separate binary classifier, with identical architecture, based on a novel document modeling technique. Namely, the classifier is implemented as a specially designed deep convolutional neural network, with injection of the document-level Mairesse features, extracted directly from the text, into an inner layer. The first layers of the network treat each sentence of the text separately; then the sentences are aggregated into the document vector. Filtering out emotionally neutral input sentences improved the performance. This method outperformed the state of the art for all five traits, and the implementation is freely available for research purposes.

Deep Learning-Based Document Modeling for Personality Detection from Text

An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.

SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

Hitherto, sentiment analysis has been mainly based on algorithms relying on the textual representation of online reviews and microblogging posts. Such algorithms are very good at retrieving texts, splitting them into parts, checking the spelling, and counting their words. But when it comes to interpreting sentences and extracting opinionated information, their capabilities are known to be very limited. Current approaches to sentiment analysis are mainly based on supervised techniques relying on manually labeled samples, such as movie or product reviews, where the overall positive or negative attitude was explicitly indicated. However, opinions do not occur only at document-level, nor they are limited to a single valence or target. Contrary or complementary attitudes toward the same topic or multiple topics can be present across the span of a review. In order to overcome this and many other issues related to sentiment analysis, we propose a novel framework, termed concept-level sentiment analysis (CLSA) model, which takes into account all the natural-language-processing tasks necessary for extracting opinionated information from text, namely: microtext analysis, semantic parsing, subjectivity detection, anaphora resolution, sarcasm detection, topic spotting, aspect extraction, and polarity detection.

/pdf/the-clsa-model-a-novel-framework-for-concept-level-sentiment-53g6gtzx5p.pdf

The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

We propose a data-driven method for automatic deception detection in real-life trial data using visual and verbal cues. Using OpenFace with facial action unit recognition, we analyze the movement of facial features of the witness when posed with questions and the acoustic patterns using OpenSmile. We then perform a lexical analysis on the spoken words, emphasizing the use of pauses and utterance breaks, feeding that to a Support Vector Machine to test deceit or truth prediction. We then try out a method to incorporate utterance-based fusion of visual and lexical analysis, using string based matching.

The Truth and Nothing But the Truth: Multimodal Analysis for Deception Detection

The continuous sophistication in clinical informationprocessing motivates the development of a dictionary likeWordNet for Medical Events in order to convey the valuableinformation (e.g., event definition, sense based contextualdescription, polarity etc.) to the experts (e.g. medicalpractitioners) and non-experts (e.g. patients) in their respective fields. The present paper reports the enrichment of medical terms such as identifying and describing events, times and the relations between them in clinical text by employing three different lexical resources namely seed list of medical events collected from SemEval 2015 Task-6, the WordNet and an English medical dictionary. In particular, we develop WordNet for Medical Events (WME) that uses contextual information for word sense disambiguation of medical terms and reduce the communication gap between doctors and patients. We have proposed two approaches (Sequential and Combined) for identifying the proper sense of a medical event based on each of the three types of texts. The polarity lexicons e.g., SentiWordNet, Affect Word List and Taboda's adjective list have been used for implementing the polarity based Word Sense Disambiguation of the medical events from their glosses as extracted from the lexicalresources. The proposed WME out-performed a previouslyproposed Lesk Word Sense Disambiguation in the range of 10-20%.

Rajiv Bajpai

Papers

A review of affective computing

SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives

The CLSA Model: A Novel Framework for Concept-Level Sentiment Analysis

The Truth and Nothing But the Truth: Multimodal Analysis for Deception Detection

Lexical Resource for Medical Events: A Polarity Based Approach