Connotation Frames: A Data-Driven Investigation

Through a particular choice of a predicate (e.g., "x violated y"), a writer can subtly connote a range of implied sentiments and presupposed facts about the entities x and y: (1) writer's perspective: projecting x as an "antagonist"and y as a "victim", (2) entities' perspective: y probably dislikes x, (3) effect: something bad happened to y, (4) value: y is something valuable, and (5) mental state: y is distressed by the event. We introduce connotation frames as a representation formalism to organize these rich dimensions of connotation using typed relations. First, we investigate the feasibility of obtaining connotative labels through crowdsourcing experiments. We then present models for predicting the connotation frames of verb predicates based on their distributional word representations and the interplay between different types of connotative relations. Empirical results confirm that connotation frames can be induced from various data sources that reflect how people use language and give rise to the connotative meanings. We conclude with analytical results that show the potential use of connotation frames for analyzing subtle biases in online news media.Comment: 11 pages, published in Proceedings of ACL 201

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

Exploiting Emotions via Composite Pretrained Embedding and Ensemble Language Model

Decisions in the modern era are based on more than just the available data; they also incorporate feedback from online sources. Processing reviews known as Sentiment analysis (SA) or Emotion analysis. Understanding the user's perspective and routines is crucial now-a-days for multiple reasons. It is used by both businesses and governments to make strategic decisions. Various architectural and vector embedding strategies have been developed for SA processing. Accurate representation of text is crucial for automatic SA. Due to the large number of languages spoken and written,  polysemy and syntactic or semantic issues were common. To get around these problems, we developed effective composite embedding (ECE), a method that combines the advantages of vector embedding techniques that are either context-independent (like glove & fasttext) or context-aware (like  XLNet) to effectively represent the features needed for processing.  To improve the performace towards emotion or  sentiment we proposed stacked ensemble model of deep lanugae models.ECE with Ensembled model is evaluated on balanced  dataset to prove that it is a reliable embedding technique and a generalised model for SA.In order to evaluate ECE, cutting-edge ML and Deep net language models are deployed and comapared. The model is evaluated using benchmark datset such as  MR, Kindle along with realtime tweet dataset of user complaints . LIME is used to verify the model's predictions and to provide statistical results for sentence.The model with ECE embedding provides state-of-art results with real time dataset as well

Affective meanings and social relations: identities and positions in the social space

Ever since Georg Simmel’s seminal works, social relations have been a central building block of sociological theory. In relational sociology, social identities are an essential concept and supposed to emerge in close interaction with other identities, discourses and objects. To assess this kind of relationality, existing research capitalises on patterns of meaning making that are constitutive for identities. These patterns are often understood as forms of declarative knowledge and are reconstructed, using qualitative methods, from denotative meanings as they surface: for example, in stories and narratives. We argue that this approach to some extent privileges explicit and conceptual knowledge over tacit and non-conceptual forms of knowledge. We suggest that affect is a concept that can adequately account for such implicit and bodily meanings, even when measured on the level of linguistic concepts. We draw on affect control theory (ACT) and related methods to investigate the affective meanings of concepts (lexemes) denoting identities in a large survey. We demonstrate that even though these meanings are widely shared across respondents, they nevertheless show systematic variation reflecting respondents’ positions within the social space and the typical interaction experiences associated with their identities. In line with ACT, we show, first, that the affective relations between exemplary identities mirror their prototypical, culturally circumscribed and institutionalised relations (for example, between role identities). Second, we show that there are systematic differences in these affective relations across gender, occupational status and regional culture, which we interpret as reflecting respondents’ subjective positioning and experience vis-à-vis a shared cultural reality