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.

The regulator accepted that the transfer of user data for security purposes seemed to be essential to the functioning of the application. But the watchdog also said the same did not apply for "business intelligence" purposes which aim to improve the apps' performance.

Machine learning is in; Flash is out. Data scientists are in great demand, specialized developers, not so much. These are just a few of the trends LinkedIn picked up in its 2017 Emerging Jobs Report. It's no surprise that jobs in tech are growing faster than any other industry. The fastest growing job over the last five years is machine learning engineer, as the number of open positions on LinkedIn has multiplied by nearly 10X.

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.

In online social networks people often express attitudes towards others, which forms massive sentiment links among users. Predicting the sign of sentiment links is a fundamental task in many areas such as personal advertising and public opinion analysis. Previous works mainly focus on textual sentiment classification, however, text information can only disclose the "tip of the iceberg" about users' true opinions, of which the most are unobserved but implied by other sources of information such as social relation and users' profile. To address this problem, in this paper we investigate how to predict possibly existing sentiment links in the presence of heterogeneous information. First, due to the lack of explicit sentiment links in mainstream social networks, we establish a labeled heterogeneous sentiment dataset which consists of users' sentiment relation, social relation and profile knowledge by entity-level sentiment extraction method. Then we propose a novel and flexible end-to-end Signed Heterogeneous Information Network Embedding (SHINE) framework to extract users' latent representations from heterogeneous networks and predict the sign of unobserved sentiment links. SHINE utilizes multiple deep autoencoders to map each user into a low-dimension feature space while preserving the network structure. We demonstrate the superiority of SHINE over state-of-the-art baselines on link prediction and node recommendation in two real-world datasets. The experimental results also prove the efficacy of SHINE in cold start scenario.

In this project we analysed how much semantic information images carry, and how much value image data can add to sentiment analysis of the text associated with the images. To better understand the contribution from images, we compared models which only made use of image data, models which only made use of text data, and models which combined both data types. We also analysed if this approach could help sentiment classifiers generalize to unknown sentiments.

Sentiment Analysis is one of the most obvious things Data Analysts with unlabelled Text data (with no score or no rating) end up doing in an attempt to extract some insights out of it and the same Sentiment analysis is also one of the potential research areas for any NLP (Natural Language Processing) enthusiasts. For an analyst, the same sentiment analysis is a pain in the neck because most of the primitive packages/libraries handling sentiment analysis perform a simple dictionary lookup and calculate a final composite score based on the number of occurrences of positive and negative words. But that often ends up in a lot of false positives, with a very obvious case being'happy' vs'not happy' – Negations, in general Valence Shifters. Consider this sentence: 'I am not very happy'. Any Primitive Sentiment Analysis Algorithm would just flag this sentence positive because of the word'happy' that apparently would appear in the positive dictionary.

Learn how to use Apache Hive to process Twitter data. The result is a list of Twitter users who sent the most tweets that contain a certain word. The steps in this document were tested on HDInsight 3.6. Linux is the only operating system used on HDInsight version 3.4 or greater. For more information, see HDInsight retirement on Windows.