Numerous studies of social media analytics (SMA) shed light upon interesting insights into the information flow in social media. As social media becomes a crucial part of human society, bridging and merging these studies could shape ideas and designs for real-world applications that allow more transparency and understanding of social media. Among several challenges of SMA, this paper focuses on two issues of 1) invasive and greedy analysis methods concerning user privacy, and 2) lack of comprehensive representations of analysis results. We use our analysis on Telegram data to propose that pursuing persona profiling using generalizing contextual analysis via Natural Language Processing (NLP) technologies could address the first problem. For the second problem, we propose to visualize the analysis results, i.e. persona profiles, to increase both comprehensibility and interpretability.
With the evolving artificial intelligence technology, the chatbots are becoming smarter and faster lately. Chatbots are typically available round the clock providing continuous support and services. A chatbot or a conversational agent is a program or software that can communicate using natural language with humans. The challenge of developing an intelligent chatbot still exists ever since the onset of artificial intelligence. The functionality of chatbots can range from business oriented short conversations to healthcare intervention based longer conversations. However, the primary role that the chatbots have to play is in understanding human utterances in order to respond appropriately. To that end, there is an increased emergence of Natural Language Understanding (NLU) engines by popular cloud service providers. The NLU services identify entities and intents from the user utterances provided as input. Thus, in order to integrate such understanding to a chatbot, this paper presents a study on existing major NLU platforms. Then, we present a case study chatbot integrated with Google DialogFlow and IBM Watson NLU services and discuss their intent recognition performance.