Organizations have generated unprecedented amounts of employee feedback through weekly or monthly pulse surveys, annual engagement surveys, and internal social networks and collaboration platforms. But many still struggle with how to efficiently comb through that mountain of information to identify actionable insights leaders can use to improve employee engagement and retention. Some companies are now turning to artificial intelligence (AI) tools to conduct sentiment analysis on employee feedback, gauge how employees feel and address their concerns. While text analysis of survey responses isn't new, the emergence of smarter algorithms enables faster and more precise search and categorization of unstructured data, such as open-ended comments, said Alan Lepofsky, vice president and principal analyst with Constellation Research, a technology research firm in Silicon Valley. Lepofsky, author of the recent report Why Artificial Intelligence Will Power the Future of Work, said vendors have made advances in sentiment analysis technology.

MeaningCloud, leader in SaaS semantic analytics, has new RapidMiner extension that offers very powerful and flexible text analytics and the ability to extract the meaning of any unstructured text.

With Firebase and Cloud Functions, you can easily add machine learning to your app to do translation, sentiment analysis, speech recognition, and computer vision. See all the talks from Google I/O '17 here: https://goo.gl/D0D4VE

Text representation in Chinese sentiment analysis is usually working at word or character level. In this paper, we prove that radical-level processing could greatly improve sentiment classification performance. In particular, we propose two types of Chinese radical-based hierarchical embeddings. The embeddings incorporate not only semantics at radical and character level, but also sentiment information. In the evaluation of our embeddings, we conduct Chinese sentiment analysis at sentence level on four different datasets. Experimental results validate our assumption that radical-level semantics and sentiments can contribute to sentence-level sentiment classification and demonstrate the superiority of our embeddings over classic textual features and popular word and character embeddings.

We report results of several experiments evaluating performance of word embeddings on semantic similarity of emotions. Our experiments suggest that the standard embeddings like GloVe and Word2Vec have very limited applicability in identifying emotions in text. Namely, using the standard arithmetic of emotions as a test, we show the mean reciprocal rank of a correct response is about 0.24, that is, combinations of word vectors are not a good proxy for expressed emotions. For example, the sum vector Joy+Fear, contrary to expectations, is not close to the vector representing Guilt. In addition, the opposite emotions, like Pessimism and Delight, have relatively high similarity to each other as word vectors (on average 0.2-0.44). Another experiment shows relatively low similarity (0.2-0.3) of word embeddings for similar emotions, such as Anger and Envy. Thus the standard methods for producing word embeddings are not adequate to represent relationships between emotion words. We conclude with a few hypotheses about improving the accuracy of embeddings in representing emotions.

Many mainstream OCR techniques involve training a character recognition model using labeled exemplary images of each individual character to be recognized. For modern printed writing, such data can be easily created by automated methods such as rasterizing appropriate font data to produce clean example images. For historical OCR in printing and writing styles distinct from those embodied in modern fonts, appropriate character images must instead be extracted from actual historical documents to achieve good recognition accuracy. For languages with small character sets it may feasible to perform this process manually, but for languages with many thousands of characters, such as Chinese, manually collecting this data is often not practical.

Sentiment analysis of tweets is often monolingual and the models provided by machine learning classifiers are usually not applicable across distinct languages. Cross-language sentiment classification usually relies on machine translation strategies in which a source language is translated to the desired target language. Machine translation is costly and the provided results are limited by the quality of the translation that is performed. In this paper, we propose an efficient translation-free deep neural architecture for performing multilingual sentiment analysis of tweets. Our proposed approach benefits from a cost-effective character-based embedding and from optimized convolutions to learn from multiple distinct languages. The resulting model is capable of learning latent features from all languages used during training at once and it does not require any translation process to be performed whatsoever. We empirically evaluate the efficiency and effectiveness of the proposed approach in tweet corpora from four different languages and we show that it presents the best trade-off among four distinct state-of-the-art deep neural architectures for sentiment analysis.

She writes about software development, UI and UX, natural language processing, Big Data, AI, and other IT-related topics.

Twitter is a popular social network where users can share short SMS-like messages called tweets. Users share thoughts, links and pictures on Twitter, journalists comment on live events, companies promote products and engage with customers. The list of different ways to use Twitter could be really long, and with 500 millions of tweets per day, there's a lot of data to analyse and to play with. This is the first in a series of articles dedicated to mining data on Twitter using Python. In this first part, we'll see different options to collect data from Twitter.

Many common mistakes can be avoided when testing sentiment data for predictive properties. The term "prediction" is not a legal definition. In assessing the predictive qualities of sentiment data there are no rules for what counts as a signal to be tested for predictive properties with regard to financial assets. However, the method you chose ultimately defines what you mean with the term "prediction". To illustrate the point: Using a more prudent definition of the term, the accuracy in the world's most famous prediction study could have been as low as 47% (7 out of 15) instead of 87% (13 out of 15%). An accuracy rate of 47% would not have produced worldwide media attention and more than 1600 academic citations, in my view.