The phrase "language sentiment" is correct and can be used in written English
It refers to the overall feeling or emotion conveyed by the words and tone of a sentence or speech. It can be used in various contexts, such as discussing the tone of a political speech or analyzing the emotional impact of a poem. Example: The language sentiment of the author's writing was melancholic, evoking a sense of sadness and nostalgia in the reader.
The system generates 487 features in five different classes: network structure and information diffusion patterns, content and language, sentiment, timing, and user meta-data.
Combining the generative system with a perceptual component of natural language sentiment analysis, we show in a third experiment that our automatically generated affect responses cause participants to show signs of increased engagement and enjoyment compared with arbitrarily chosen comparable motion parameters.
Experiments performed for cross-language sentiment classification showed the proposed CL-SCL had competitive results.
The experiments show that the proposed approach is feasible for cross-language sentiment classification tasks and provides insight into the semantic relationship between two different languages.
"Words and language and sentiment and ideas, right?
Yet the more she sustains this strict decorum of language and sentiment, the more feeling presses against its surface: her stories are emotionally taxing, despite their reserve.
The experiments focused on multiple language text sentiment classification where English is used in the source and three other languages are separately used in the target.
A common currency in Europe might work in spite of the barriers of language and sentiment if there were real deregulation of the labor market.
The API allows developers to quickly extract basic data like headlines and the body of text from a document, but it also offers a summarization service, entity and concept extraction, and language and sentiment detection.
Google is intending to expand the dev tools available to makers using the Raspberry Pi microprocessor to power their projects this year — potentially offering software tools for face- and emotion-recognition, speech-to-text translation, natural language processing, sentiment analysis, predictive analytics and more.
Experiments are performed for three applications which include image classification (where 31 unique classes are defined), multi-language text document classification (where six unique classes are defined), and multi-language text sentiment classification.
