In essence, sentiment analysis is the process of gauging the emotional tone behind a series of words, used to gain an understanding of the emotions, attitudes and opinions expressed within a customer's online mentions. Real-world examples include the Obama administration using SA to measure public responses to campaign messages ahead of 2012 presidential election, and Expedia Canada taking advantage of SA to quickly understand negative consumer attitudes to the music used in one of their adverts.

Check out "Text Mining with R: A tidy approach" to learn about how tidy data principles and the tidytext package can help you perform text mining in R. "Many of us who work in analytical fields are not trained in even simple interpretation of natural language," write Julia Silge, Ph.D., and David Robinson, Ph.D., in their newly released book Text Mining with R: A tidy approach. The applications of text mining are numerous and varied, though; sentiment analysis can assess the emotional content of text, frequency measurements can identify a document's most important terms, analysis can explore relationships and connections between words, and topic modeling can classify and cluster similar documents. I recently caught up with Silge and Robinson to discuss how they're using text mining on job postings at Stack Overflow, some of the challenges and best practices they've experienced when mining text, and how their tidytext package for R aims to make text analysis both easy and informative. Text and other unstructured data is increasingly important for data analysts and data scientists in diverse fields from health care to tech to nonprofits. This data can help us make good decisions, but to capitalize on it, we must have the tools and the skills to get from unstructured text to insights.

We use and compare various different methods for sentiment analysis on tweets (a binary classification problem). The training dataset is expected to be a csv file of type tweet_id,sentiment,tweet where the tweet_id is a unique integer identifying the tweet, sentiment is either 1 (positive) or 0 (negative), and tweet is the tweet enclosed in "". Similarly, the test dataset is a csv file of type tweet_id,tweet. Please note that csv headers are not expected and should be removed from the training and test datasets. There are some general library requirements for the project and some which are specific to individual methods.

Natural language processing (NLP) is a technology spawned from the need for machines to understand and communicate with humans in human language, not formal computer languages. The concept behind NLP is simple: if and when machines can understand and communicate with humans in natural (human) language, it democratizes data science, enabling humans to access, analyze, and leverage data more intelligently and become more efficient as they offload redundant, data-heavy tasks to machines. NLP is most commonly understood as a user interface (UI) technology, enabling two-way communications with computers via speech or text. However, NLP is also a critical technology for extracting insights and analysis from a vast amount of previously unindexed and unstructured data; mining video and audio files, emails, scanned documents, and more. NLP adoption is accelerating, but not because of the creation of new NLP algorithms, as the data science in that regard is mature.

Any-gram kernels are a flexible and efficient way to employ bag-of-n-gram features when learning from textual data. They are also compatible with the use of word embeddings so that word similarities can be accounted for. While the original any-gram kernels are implemented on top of tree kernels, we propose a new approach which is independent of tree kernels and is more efficient. We also propose a more effective way to make use of word embeddings than the original any-gram formulation. When applied to the task of sentiment classification, our new formulation achieves significantly better performance.

Scientific publications have evolved several features for mitigating vocabulary mismatch when indexing, retrieving, and computing similarity between articles. These mitigation strategies range from simply focusing on high-value article sections, such as titles and abstracts, to assigning keywords, often from controlled vocabularies, either manually or through automatic annotation. Various document representation schemes possess different cost-benefit tradeoffs. In this paper, we propose to model different representations of the same article as translations of each other, all generated from a common latent representation in a multilingual topic model. We start with a methodological overview on latent variable models for parallel document representations that could be used across many information science tasks. We then show how solving the inference problem of mapping diverse representations into a shared topic space allows us to evaluate representations based on how topically similar they are to the original article. In addition, our proposed approach provides means to discover where different concept vocabularies require improvement.

Bear with me, it's more interesting than it sounds:). Yes, this (46-page) paper does include a catalogue of data sets with dialogues from different domains, but it also includes a high level survey of techniques that are used in building dialogue systems (aka chatbots). In particular, it focuses on data-driven systems, i.e. those that incorporate some kind of learning from data. This particular paper is focused on corpus-based learning where you have been able to build up, or have access to, a data set on which you can train your models. If you want to build a defensible machine learning based business, having access to quality sources of data that your competitors don't is a good start.

The modern world seems really fast and dynamic with a multitude of new products being launched. Marketing agencies are making fortune by monitoring the markets and delivering reports on consumers' opinions. For today, the feedback analysis is a separate area, let's say a growing industry with an array of products and services. And the prices for those services are pretty exorbitant. So, do vendors have a chance to cut down expenses?

No knowledge of machine learning is required for using Signals model builder. Just choose from a variety of indicators, ranging from traditional technical analysis to deep learning or sentiment analysis based on media monitoring and combine them together. However, if you happen to be a developer or a data scientist you can develop new trading indicators from scratch and monetize your data science skills through Signals indicator marketplace.

News have been de-duplicated based on the title. Finally, TICKER, PUBLICATION_DATE and SUMMARY columns were kept. Sentiment Analysis was performed on the SUMMARY column using Loughran and McDonald Financial Sentiment Dictionary for financial sentiment analysis, implemented in the pysentiment python library. This library offers both a tokenizer, that performs also stemming and stop words removal, and a method to score a tokenized text.