Information extraction from multimedia web documents: an open-source platform and testbed

The LivingKnowledge project aimed to enhance the current state of the art in search, retrieval and knowledge management on the web by advancing the use of sentiment and opinion analysis within multimedia applications. To achieve this aim, a diverse set of novel and complementary analysis techniques have been integrated into a single, but extensible software platform on which such applications can be built. The platform combines state-of-the-art techniques for extracting facts, opinions and sentiment from multimedia documents, and unlike earlier platforms, it exploits both visual and textual techniques to support multimedia information retrieval. Foreseeing the usefulness of this software in the wider community, the platform has been made generally available as an open-source project. This paper describes the platform design, gives an overview of the analysis algorithms integrated into the system and describes two applications that utilise the system for multimedia information retrieval

Do sentiment indicators help to assess and predict actual developments of the Chinese economy?

This paper evaluates the usefulness of business sentiment indicators for forecasting developments in the Chinese real economy. We use data on diffusion indices collected by the People’s Bank of China for forecasting industrial production, retail sales and exports. Our bivariate vector autoregressive models, each composed of one diffusion index and one real sector variable, generally outperform univariate AR models in forecasting one to four quarters ahead. Similarly, principal components analysis, combining information from various diffusion indices, leads to enhanced forecasting performance. Our results indicate that Chinese business sentiment indicators convey useful information about current and future developments in the real economy. They also suggest that the official data provide a fairly accurate picture of the Chinese economy.forecasting; diffusion index; VAR; China

Identifying Citation Sentiment and its Influence while Indexing Scientific Papers

Sentiment analysis has proven to be a popular research area for analyzing social media texts, newspaper articles, and product reviews. However, sentiment analysis of citation instances is a relatively unexplored area of research. For scientific papers, it is often assumed that the sentiment associated with citation instances is inherently positive. This assumption is due to the hedged nature of sentiment in citations, which is difficult to identify and classify. As a result, most of the existing indexes focus only on the frequency of citation. In this paper, we highlight the importance of considering the sentiment of citation while preparing ranking indexes for scientific literature. We perform automatic sentiment classification of citation instances on the ACL Anthology collection of papers. Next, we use the sentiment score in addition to the frequency of citation to build a ranking index for this collection of scientific papers. By using various baselines, we highlight the impact of our index on the ACL Anthology collection of papers. Our research contributes toward building more sentiment sensitive ranking index which better underlines the influence and usefulness of research papers

Noise or music? Investigating the usefulness of normalisation for robust sentiment analysis on social media data

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvestigated. Sentiment analysis approaches mostly include spell checking or rule-based normalisation as preprocess- ing and rarely investigate its impact on the task performance. We present an optimised sentiment classifier and investigate to what extent its performance can be enhanced by integrating SMT-based normalisation as preprocessing. Experiments on a test set comprising a variety of user-generated content genres revealed that normalisation improves sentiment classification performance on tweets and blog posts, showing the model’s ability to generalise to other data genres

Expressive speech synthesis using sentiment embeddings

In this paper we present a DNN based speech synthesis system trained on an audiobook including sentiment features predicted by the Stanford sentiment parser. The baseline system uses DNN to predict acoustic parameters based on conventional linguistic features, as they have been used in statistical parametric speech synthesis. The predicted parameters are transformed into speech using a conventional high-quality vocoder. In this paper, the conventional linguistic features are enriched using sentiment features. Different sentiment representations have been considered, combining sentiment probabilities with hierarchical distance and context. After preliminary analysis a listening experiment is conducted, where participants evaluate the different systems. The results show the usefulness of the proposed features and reveal differences between expert and non-expert TTS user.Peer ReviewedPostprint (published version

Opinion context extraction for aspect sentiment analysis.

Sentiment analysis is the computational study of opinionated text and is becoming increasing important to online commercial applications. However, the majority of current approaches determine sentiment by attempting to detect the overall polarity of a sentence, paragraph, or text window, but without any knowledge about the entities mentioned (e.g. restaurant) and their aspects (e.g. price). Aspect-level sentiment analysis of customer feedback data when done accurately can be leveraged to understand strong and weak performance points of businesses and services, and can also support the formulation of critical action steps to improve performance. In this paper we focus on aspect-level sentiment classification, studying the role of opinion context extraction for a given aspect and the extent to which traditional and neural sentiment classifiers benefit when trained using the opinion context text. We propose four methods to aspect context extraction using lexical, syntactic and sentiment co-occurrence knowledge. Further, we evaluate the usefulness of the opinion contexts for aspect-sentiment analysis. Our experiments on benchmark data sets from SemEval and a real-world dataset from the insurance domain suggests that extracting the right opinion context is effective in improving classification performance.Specifically combining syntactical features with sentiment co-occurrence knowledge leads to the best aspect-sentiment classification performance

Sentiment analysis – an overview of a technique which can be used in marketing activities

A sentiment analysis, a form of artificial intelligence, is a technique which uses natural language processing (NLP) to ascertain the opinions and emotional tone of the user written content on the online platform. It can be used in any form ranging from determining the sentiments of consumer’s reviews, employee’s feedback, and their social presence for effective marketing of their products and services. Through this article we wish to analyse the existing literature in sentiment analysis field to ascertain it usefulness in the marketing activities

Early Public Outlook on the Coronavirus Disease (COVID-19): A Social Media Study

The recent outbreak of the coronavirus (COVID-19) brought with its public concerns and fears about a global epidemic. With the increase in the popularity, usage, and reach of social media, this research examined the early public outlook on COVID-19 using SM-Platform, Twitter.com. The current study employed a mixed-method approach in collecting and analyzing public tweets by combining quantitative sentiment analysis with a qualitative thematic analysis. Our results revealed positive sentiment prior to the spread of the disease. The sentiment then turned negative as the disease spread, accompanied by a large amount of fear as rumors. In a thematic analysis we also uncovered nine key topics on the disease including, but not limited to, prevention, symptoms and spread of disease. Our study will provide an understanding of social media and public health outbreak surveillance. The findings of the research revealed the usefulness of twitter mining to illuminate public health education