Summarization significantly impacts sentiment analysis across languages with diverse morphologies. This study examines extractive and abstractive summarization effects on sentiment classification in English, German, French, Spanish, Italian, Finnish, Hungarian, and Arabic. We assess sentiment shifts post-summarization using multilingual transformers (mBERT, XLM-RoBERTa, T5, and BART) and language-specific models (FinBERT, AraBERT). Results show extractive summarization better preserves sentiment, especially in morphologically complex languages, while abstractive summarization improves readability but introduces sentiment distortion, affecting sentiment accuracy. Languages with rich inflectional morphology, such as Finnish, Hungarian, and Arabic, experience greater accuracy drops than English or German. Findings emphasize the need for language-specific adaptations in sentiment analysis and propose a hybrid summarization approach balancing readability and sentiment preservation. These insights benefit multilingual sentiment applications, including social media monitoring, market analysis, and cross-lingual opinion mining.

Bengaluru: With an increased demand for advanced artificial intelligence (AI) across businesses, IBM has rolled out advanced AI based solutions under Watson that can read and analyse complex English language including meaning of idioms. The technology major said this advanced version of natural language processing (NLP) for its Project Debater would have the capability to understand challenging aspects of conversational English helping organisations to get greater clarity and more insights from their data. IBM said these new technologies represent the first commercialization of key NLP capabilities to come from IBM Research's Project Debater. Subram Natrajan, IBM India CTO, said today NLP has many limitations and this is going to push boundaries. For instance, he said, the Advanced Sentiment Analysis solution, to be integrated into Watson, is going to have a huge impact in terms of understanding the sentiment behind a language.

Artificial Intelligence (AI) is having a transformative effect on the business world and the $600 billion global legal services market is not immune. As AI automates basic processes, in the legal profession it promises to allow lawyers devote their time to more valuable, cost-effective, and strategic work. Consultants at McKinsey & Company estimate that 22% of a lawyer's job and 35% of a paralegal's job can be automated. However, the common perception among lawyers remains that machines cannot yet match the legal intellect of human lawyers in daily fundamentals of the profession. This assumption was tested in the first of its kind "AlphaGo"-style Study in the legal profession.

Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.