Social media marketing is an inevitable marketing strategy of every business, here is the analysis of how ai helps to boost social media marketing campaign. Growth of data science and implementation of artificial intelligence in marketing campaigns are helping social media marketers to save lots of time and effort. Social media marketing is an unavoidable part of every business marketing strategy because this age has become the social media era. According to Social media today, Roughly 45% of the world's population uses social media and they spend an average of 2 hours and 23 minutes per day on social media. Social media is not only used to promote your products/services or sharing news but it also an effective and powerful tool for reaching new clients, increased brand awareness and customer support.

Code-mixing is the practice of alternating between two or more languages. Mostly observed in multilingual societies, its occurrence is increasing and therefore its importance. A major part of sentiment analysis research has been monolingual, and most of them perform poorly on code-mixed text. In this work, we introduce methods that use different kinds of multilingual and cross-lingual embeddings to efficiently transfer knowledge from monolingual text to code-mixed text for sentiment analysis of code-mixed text. Our methods can handle code-mixed text through a zero-shot learning. Our methods beat state-of-the-art on English-Spanish code-mixed sentiment analysis by absolute 3\% F1-score. We are able to achieve 0.58 F1-score (without parallel corpus) and 0.62 F1-score (with parallel corpus) on the same benchmark in a zero-shot way as compared to 0.68 F1-score in supervised settings. Our code is publicly available.

There exist today state-of-the-art computational models that can provide highly accurate predictions about complex phenomena such as crop growth and weather patterns. However, certain phenomena, such as food insecurity, involve a host of factors that cannot be modeled by any single one of these models, but which instead require the integration of multiple models. To truly integrate these computational models, it is necessary to'lift' them to a common representation that is (i) agnostic to the software implementation, (ii) semantically rich enough to represent the implicit domain knowledge in the models, and (iii) connected to the domain literature. The AutoMATES project aims to build technology to construct and curate semantically-rich representations of scientific models by integrating three different sources of information: - natural language descriptions of models in publications and other technical documentation, - the equations contained in these documents, and - the software the implements these models. An example of a model being represented in these three forms (text, equations, and software) is shown in Figure 1. This model is a differential equation describing the biophysical variable, leaf area index (LAI). The network on the right half of the figure is an aspirational representation of the model as a Bayesian network. Although this example is handcrafted, our end goal is to be able to automatically assemble models with this level of semantic richness.

Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

In the 10 days leading up to Christmas this year, I searched on Instagram for three of my exes, an acquaintance I met on a trip to Cuba four years ago, an account dedicated to astrology memes, a past roommate, my own dog's account (@lucythetherapypup), my best friend's sweater-wearing poodle, a famous Pomeranian who lives in New York, a bird named Parfait I recently met at a San Francisco market, 10 contestants of the reality TV show Love Island, and the hashtag #wienerdog. I know all of this because Instagram told me. That's because this month, I submitted a data request under California's new privacy law to see just how much information the company has on me. What I got was a wide-ranging look at how my life has changed in the last 10 years since I first logged on to Instagram, and a window into what the company is willing to share about what it knows about me. Under the California Consumer Privacy Act, I have the right to demand companies disclose "any personal information" they collect about me and request a copy of that information.

How do you test an application which constantly listens to the customers, learns their behaviour and create personalised engagements based out of learnings!! Today data plays a vital role in every decision making and hence making sense of the data to derive useful insights for our customers is a key for success. Sentiment Analysis is the process of classifying the data into positive, negative or neutral implemented using natural language processing (NLP) and Machine Learning techniques that helps in analysing the data to gauge public opinion, market research, monitor brand and product reputation, and understand customer experiences and is mostly offered as Sentiment Analysis as-a-Service . In this talk we will discuss the Challenges are around analysing, explicit and implict opinions, sarcasm, comparative opinions, Multilingual, Emojis, defination on neutral to just name a few and the strategies to test such applications with a use case on Airlines Sentiment (trained with tweets about airlines to identify between positive, neutral, and negative tweets).

Sentiment is a huge driving factor in the cryptocurrency market. But it is a metric which is very hard to measure. Sentiment analysis has been on the rise for the past few years. With the introduction of new packages, sentiment analysis can be done more quickly and efficiently than ever. In this post, you'll see why looking at the mood on the social media is not a great idea for sentiment analysis.

What does Minority Report, Black Mirror, and 1984 all have in common? Well, turn up to the talk to find out. On a day to day basis we countlessly write notes, send messages and respond to emails. The question is: what does what we write actually show about us, and how can we use the meaning behind these pieces of text to predict crimes and attacks. This talk delves into just this - how machine learning, and specifically natural language processing and sentiment analysis, can be used to predict crime and security attacks.

In this particular case, in only 10 seconds and with the small dataset provided, the CLI tool was able to run quite a few iterations, meaning training multiple times based on different combinations of algorithms/configuration with different internal data transformations and algorithm's hyper-parameters. Finally, the "best quality" model found in 10 seconds is a model using a particular trainer/algorithm with any specific configuration. Depending on the exploration time, the command can produce a different result. The selection is based on the multiple metrics shown, such as Accuracy. The first and easiest metric to evaluate a binary-classification model is the accuracy, which is simple to understand. "Accuracy is the proportion of correct predictions with a test data set.".

Article includes shout outs to Doug Laney ('The 3 Vs of Big Data'), Cathy O'Neil "Weapons of #Math Destruction) & Tony's'Big on Data bro' Andrew J. Brust.