Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities. The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities. However, existing work learns joint representations by requiring all modalities as input and as a result, the learned representations may be sensitive to noisy or missing modalities at test time. With the recent success of sequence to sequence (Seq2Seq) models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time. In this paper, we propose a method to learn robust joint representations by translating between modalities. Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input. We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities. Once our translation model is trained with paired multimodal data, we only need data from the source modality at test time for final sentiment prediction. This ensures that our model remains robust from perturbations or missing information in the other modalities. We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube. Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to missing or perturbed modalities.

A new report released Monday reveals that the Internet Research Agency, the troll farm linked to Russian intelligence, actually increased its social media activity after the 2016 election. The report, which took seven months to complete and is the most comprehensive of its kind to date, comes from researchers at Oxford University and analytics firm Graphika. Their data shows the volume of IRA activity doubling between 2016 and 2017 on Facebook, Instagram, and Twitter, even as the number of ads purchased by the agency decreased. The amount of activity increased the most on Facebook-owned Instagram, where it more than doubled from 2,611 posts in 2016 to 5,956 posts in 2017. The research is based on Facebook data from 2015-2017, Twitter data from 2009-2018, and YouTube data from 2014-2018 that was provided by the companies to the Senate Intelligence Committee and relayed to the researchers.

Here a sentiment analysis based on the tweets published from 7th of Novemember 2018 about BITCOIN, and saved on our database. The program analyzed 28254 Tweets, and labeled 21995 as SPAM or USELESS, and 6259 as decent QUALITY or USEFUL. Our Artificial Intelligence for sentiment analysis, marked 105 tweets as Angry, 24 as Fear, 5 as Bored, 3 as Sarcasm, 346 as Excited, 53 as Sad, and 493 as Happy. Is that increasing/consistent sell volume vs decreasing buy volume but price is increasing? Chinese left the property market because of tighter capital flight controls imposed by their government.

Sentiment analysis (SA) is a task related to understanding people's feelings in written text; the starting point would be to identify the polarity level (positive, neutral or negative) of a given text, moving on to identify emotions or whether a text is humorous or not. This task has been the subject of several research competitions in a number of languages, e.g., English, Spanish, and Arabic, among others. In this contribution, we propose an SA system, namely EvoMSA, that unifies our participating systems in various SA competitions, making it domain independent and multilingual by processing text using only language-independent techniques. EvoMSA is a classifier, based on Genetic Programming, that works by combining the output of different text classifiers and text models to produce the final prediction. We analyze EvoMSA, with its parameters fixed, on different SA competitions to provide a global overview of its performance, and as the results show, EvoMSA is competitive obtaining top rankings in several SA competitions. Furthermore, we performed an analysis of EvoMSA's components to measure their contribution to the performance; the idea is to facilitate a practitioner or newcomer to implement a competitive SA classifier. Finally, it is worth to mention that EvoMSA is available as open source software.

Few concepts in professional life stir more ideas and emotions than Artificial Intelligence (AI). There are passionate differences of opinion about what it is and what it's capable of, whether it will simply destroy jobs or create them as well, whether it can improve our human capabilities or make them redundant. Nowhere will you find a broader and more representative range of these opinions than in the AI conversation on LinkedIn. Analysing it reveals important clues about our understanding of AI. We can see the thought-leaders and themes dominating the conversation, the motives that business leaders and technology companies have, and the likely consequences for different industries and sectors.

Mining financial text documents and understanding the sentiments of individual investors, institutions and markets is an important and challenging problem in the literature. Current approaches to mine sentiments from financial texts largely rely on domain specific dictionaries. However, dictionary based methods often fail to accurately predict the polarity of financial texts. This paper aims to improve the state-of-the-art and introduces a novel sentiment analysis approach that employs the concept of financial and non-financial performance indicators. It presents an association rule mining based hierarchical sentiment classifier model to predict the polarity of financial texts as positive, neutral or negative. The performance of the proposed model is evaluated on a benchmark financial dataset. The model is also compared against other state-of-the-art dictionary and machine learning based approaches and the results are found to be quite promising. The novel use of performance indicators for financial sentiment analysis offers interesting and useful insights.

Aspect-level sentiment classification (ASC) aims at identifying sentiment polarities towards aspects in a sentence, where the aspect can behave as a general Aspect Category (AC) or a specific Aspect Term (AT). However, due to the especially expensive and labor-intensive labeling, existing public corpora in AT-level are all relatively small. Meanwhile, most of the previous methods rely on complicated structures with given scarce data, which largely limits the efficacy of the neural models. In this paper, we exploit a new direction named coarse-to-fine task transfer, which aims to leverage knowledge learned from a rich-resource source domain of the coarse-grained AC task, which is more easily accessible, to improve the learning in a low-resource target domain of the fine-grained AT task. To resolve both the aspect granularity inconsistency and feature mismatch between domains, we propose a Multi-Granularity Alignment Network (MGAN). In MGAN, a novel Coarse2Fine attention guided by an auxiliary task can help the AC task modeling at the same fine-grained level with the AT task. To alleviate the feature false alignment, a contrastive feature alignment method is adopted to align aspect-specific feature representations semantically. In addition, a large-scale multi-domain dataset for the AC task is provided. Empirically, extensive experiments demonstrate the effectiveness of the MGAN.

What do you do before purchasing something that costs more than a pack of gum? Whether you want to treat yourself to new sneakers, a laptop, or an overseas tour, processing an order without checking out similar products or offers and reading reviews doesn't make much sense anymore. Thanks to comment sections on eCommerce sites, social nets, review platforms, or dedicated forums, you can learn a ton about a product or service and evaluate whether it's a good value for money. Other customers, including your potential clients, will do all the above. People's desire to engage with businesses and the overall brand perception depends heavily on public opinion.

Almost three years after Facebook was first alerted to a potential data breach committed by Cambridge Analytica, it is unclear how many people still have access to the data stolen by the company. The findings come in a report released Tuesday by the Information Commissioner's Office, a government agency in the UK that reports to Parliament. The investigation, which launched in May 2017, analyzed over 50 million pages of data seized from the now-defunct Cambridge Analytica. According to interviews with Cambridge Analytica employees, multiple attempts were made to delete the Facebook data misused by the company, but there still might be ad targeting tools that are based on data harvested by Facebook that have not been deleted. Facebook first learned of data leaks in 2015 when The Guardian reported Cambridge Analytica's involvement with Ted Cruz's presidential campaign.

This research aims to identify how Bitcoin-related news publications and online discourse are expressed in Bitcoin exchange movements of price and volume. Being inherently digital, all Bitcoin-related fundamental data (from exchanges, as well as transactional data directly from the blockchain) is available online, something that is not true for traditional businesses or currencies traded on exchanges. This makes Bitcoin an interesting subject for such research, as it enables the mapping of sentiment to fundamental events that might otherwise be inaccessible. Furthermore, Bitcoin discussion largely takes place on online forums and chat channels. In stock trading, the value of sentiment data in trading decisions has been demonstrated numerous times [1] [2] [3], and this research aims to determine whether there is value in such data for Bitcoin trading models. To achieve this, data over the year 2015 has been collected from Bitcointalk.org, (the biggest Bitcoin forum in post volume), established news sources such as Bloomberg and the Wall Street Journal, the complete /r/btc and /r/Bitcoin subreddits, and the bitcoin-otc and bitcoin-dev IRC channels. By analyzing this data on sentiment and volume, we find weak to moderate correlations between forum, news, and Reddit sentiment and movements in price and volume from 1 to 5 days after the sentiment was expressed. A Granger causality test confirms the predictive causality of the sentiment on the daily percentage price and volume movements, and at the same time underscores the predictive causality of market movements on sentiment expressions in online communities