10,382 research outputs found
Volatility Prediction using Financial Disclosures Sentiments with Word Embedding-based IR Models
Volatility prediction--an essential concept in financial markets--has
recently been addressed using sentiment analysis methods. We investigate the
sentiment of annual disclosures of companies in stock markets to forecast
volatility. We specifically explore the use of recent Information Retrieval
(IR) term weighting models that are effectively extended by related terms using
word embeddings. In parallel to textual information, factual market data have
been widely used as the mainstream approach to forecast market risk. We
therefore study different fusion methods to combine text and market data
resources. Our word embedding-based approach significantly outperforms
state-of-the-art methods. In addition, we investigate the characteristics of
the reports of the companies in different financial sectors
CARBD-Ko: A Contextually Annotated Review Benchmark Dataset for Aspect-Level Sentiment Classification in Korean
This paper explores the challenges posed by aspect-based sentiment
classification (ABSC) within pretrained language models (PLMs), with a
particular focus on contextualization and hallucination issues. In order to
tackle these challenges, we introduce CARBD-Ko (a Contextually Annotated Review
Benchmark Dataset for Aspect-Based Sentiment Classification in Korean), a
benchmark dataset that incorporates aspects and dual-tagged polarities to
distinguish between aspect-specific and aspect-agnostic sentiment
classification. The dataset consists of sentences annotated with specific
aspects, aspect polarity, aspect-agnostic polarity, and the intensity of
aspects. To address the issue of dual-tagged aspect polarities, we propose a
novel approach employing a Siamese Network. Our experimental findings highlight
the inherent difficulties in accurately predicting dual-polarities and
underscore the significance of contextualized sentiment analysis models. The
CARBD-Ko dataset serves as a valuable resource for future research endeavors in
aspect-level sentiment classification
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
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