Should being comparable to BERT really be your goal here? The thing about BERT is that it wasn't really specifically designed for sentiment analysis. It just happens that it does that well too. But there's no reason to believe it's anywhere close to the "best way" to do sentiment analysis. I mean, as an analogy, pulling out a calculator to make a quick computation is often more convenient than booting up Matlab to do it, but using this fact to extol the merits of calculator kind of misses the point. If you want to describe how good your model is, you really should choose more relevant comparisons.

While it's not uncommon for small and medium-sized businesses (SMBs) to switch financial institutions, the 2019 FIS Performance Against Customer Expectations report has found that the rate of churn is increasing. Historically, 13%-15% of small and medium-sized firms have been found to be actively reviewing their banking relationships. However, the turnover rate has now risen to 61% among the top 50 U.S. banks and 60% among regional banks. All it may take to push an already skeptical firm to switch is one more bad experience. So customer sentiment analysis could be exactly what financial institutions need to improve customer experience -- ideally, before things ever reach that pass.

Sentiment analysis benefits from large, hand-annotated resources in order to train and test machine learning models, which are often data hungry. While some languages, e.g., English, have a vast arrayof these resources, most under-resourced languages do not, especially for fine-grained sentiment tasks, such as aspect-level or targeted sentiment analysis. To improve this situation, we propose a cross-lingual approach to sentiment analysis that is applicable to under-resourced languages and takes into account target-level information. This model incorporates sentiment information into bilingual distributional representations, byjointly optimizing them for semantics and sentiment, showing state-of-the-art performance at sentence-level when combined with machine translation. The adaptation to targeted sentiment analysis on multiple domains shows that our model outperforms other projection-based bilingual embedding methods on binary targetedsentiment tasks. Our analysis on ten languages demonstrates that the amount of unlabeled monolingual data has surprisingly little effect on the sentiment results. As expected, the choice of a annotated source language for projection to a target leads to better results for source-target language pairs which are similar. Therefore, our results suggest that more efforts should be spent on the creation of resources for less similar languages tothose which are resource-rich already. Finally, a domain mismatch leads to a decreased performance. This suggests resources in any language should ideally cover varieties of domains.

Quertle is an artificial intelligence company focused on text discovery and understanding in the biomedical and life sciences fields. Published information is the foundation for the entire healthcare industry – from basic research to drug discovery to clinical trials to healthcare delivery and everything in between including business aspects. Quertle's flagship product Qinsight enables unparalleled discovery of literature through AI-powered searching, integration, organization, and presentation including predictive visual analytics. Qinsight, which covers journal articles, patents, clinical trials, treatment protocols and much more, is in use by pharmaceutical and biotechnology companies, universities, research centers, and healthcare providers around the world. Quertle was founded by Jeffrey Saffer and Vicki Burnett – Ph.D. biomedical scientists who were frustrated with the inefficiencies in discovering critical publications and the waste caused by missing information.

Quertle is an artificial intelligence company focused on text discovery and understanding in the biomedical and life sciences fields. Published information is the foundation for the entire healthcare industry – from basic research to drug discovery to clinical trials to healthcare delivery and everything in between including business aspects. Quertle's flagship product Qinsight enables unparalleled discovery of literature through AI-powered searching, integration, organization, and presentation including predictive visual analytics. Qinsight, which covers journal articles, patents, clinical trials, treatment protocols and much more, is in use by pharmaceutical and biotechnology companies, universities, research centers, and healthcare providers around the world. Quertle was founded by Jeffrey Saffer and Vicki Burnett – Ph.D. biomedical scientists who were frustrated with the inefficiencies in discovering critical publications and the waste caused by missing information.

Multimodal language analysis often considers relationships between features based on text and those based on acoustical and visual properties. Text features typically outperform non-text features in sentiment analysis or emotion recognition tasks in part because the text features are derived from advanced language models or word embeddings trained on massive data sources while audio and video features are human-engineered and comparatively underdeveloped. Given that the text, audio, and video are describing the same utterance in different ways, we hypothesize that the multimodal sentiment analysis and emotion recognition can be improved by learning (hidden) correlations between features extracted from the outer product of text and audio (we call this text-based audio) and analogous text-based video . This paper proposes a novel model, the Interaction Canonical Correlation Network (ICCN), to learn such multimodal embeddings. ICCN learns correlations between all three modes via deep canonical correlation analysis (DCCA) and the proposed embeddings are then tested on several benchmark datasets and against other state-of-the-art multimodal embedding algorithms. Empirical results and ablation studies confirm the effectiveness of ICCN in capturing useful information from all three views.

Institutions may also consider building different machine learning models for the different regions, countries and languages they operate in.

Episode 167 - Kevin Craine and Billee Howard discuss the use of nuero-powered technology to quantify, measure and understand human thought. Explore how to use artificial intelligence and sentiment analysis to connect customer emotion directly to improved business performance. Understand the convergence of'big emotion' and'big data' and how it is valuable from a strategic and marketing perspective. Stay tuned for three action items in the second half. Do you want to be a guest?

Sign in to report inappropriate content. This session of Dr. Spotfire featured Neil Kanungo, Data Scientist at TIBCO Software Inc. Discover what Dr. Spotfire's online office hours has to offer by registering for a live session. If you are ready to showcase interesting visuals and gain deeper insights into your data, join the conversation on Twitter using the #DrSpotfire hashtag and then post your question to the TIBCO Community "Answers" section with the hashtag #DrSpotfire.

Every natural text is written in some style. The style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. Some factors implicitly reflect the author's personality, while others are explicitly controlled by the author's choices in order to achieve some personal or social goal. One cannot form a complete understanding of a text and its author without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the covarying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides a benchmark corpus (xSLUE) with an online platform (http://xslue.com) for cross-style language understanding and evaluation. The benchmark contains text in 15 different styles and 23 classification tasks. For each task, we provide the fine-tuned classifier for further analysis. Our analysis shows that some styles are highly dependent on each other (e.g., impoliteness and offense), and some domains (e.g., tweets, political debates) are stylistically more diverse than others (e.g., academic manuscripts). We discuss the technical challenges of cross-style understanding and potential directions for future research: cross-style modeling which shares the internal representation for low-resource or low-performance styles and other applications such as cross-style generation.