For our case we only consider Text feature of the Tweet as we are interested on the review of the movie. We can also use the other features such as Latitude/Longitude, replied to, etc. do other analysis on the tweeted data.

About indico indico provides state-of-the-art machine learning algorithms for text and image analysis in the form of a simple to use web service. This, for the first time, enables companies to automatically extract meaningful insight from unstructured data regardless of their size or capability. Sentiment Analysis, Social Media Monitoring, Content Filtering, Content Classification, Recommendation, and Personalization are just some of the areas in which indico's customers are deploying its technology to improve business outcomes. Furthermore, indico's rapid customization capabilities have also enabled companies such as Mavrck, CO Everywhere, and interlinkONE to quickly develop compelling new solutions that weren't practical before.

This paper considers survey prediction from social media. We use topic models to correlate social media messages with survey outcomes and to provide an interpretable representation of the data. Rather than rely on fully unsupervised topic models, we use existing aggregated survey data to inform the inferred topics, a class of topic model supervision referred to as collective supervision. We introduce and explore a variety of topic model variants and provide an empirical analysis, with conclusions of the most effective models for this task.

Sentiment word identification is a fundamental work in numerous applications of sentiment analysis and opinion mining, such as review mining, opinion holder finding, and twitter classification. In this paper, we propose an optimization model with L1 regularization, called ISOMER, for identifying the sentiment words from the corpus. Our model can employ both seed words and documents with sentiment labels, different from most existing researches adopting seed words only. The L1 penalty in the objective function yields a sparse solution since most candidate words have no sentiment. The experiments on the real datasets show that ISOMER outperforms the classic approaches, and that the lexicon learned by ISOMER can be effectively adapted to document-level sentiment analysis.

The analysis of interactions between social media and traditional news streams is becoming increasingly relevant for a variety of applications, including: understanding the underlying factors that drive the evolution of data sources, tracking the triggers behind events, and discovering emerging trends.Researchers have explored such interactions by examining volume changes or information diffusions,however, most of them ignore the semantical and topical relationships between news and social media data.Our work is the first attempt to study how news influences social media, or inversely, based on topical knowledge.We propose a hierarchical Bayesian model that jointly models the news and social media topics and their interactions.We show that our proposed model can capture distinct topics for individual datasets as well as discover the topic influences among multiple datasets.By applying our model to large sets of news and tweets, we demonstrate its significant improvement over baseline methods and explore its power in the discovery of interesting patterns for real world cases.

It has been shown that learning distributed word representations is highly useful for Twitter sentiment classification.Most existing models rely on a single distributed representation for each word.This is problematic for sentiment classification because words are often polysemous and each word can contain different sentiment polarities under different topics.We address this issue by learning topic-enriched multi-prototype word embeddings (TMWE).In particular, we develop two neural networks which 1) learn word embeddings that better capture tweet context by incorporating topic information, and 2) learn topic-enriched multiple prototype embeddings for each word.Experiments on Twitter sentiment benchmark datasets in SemEval 2013 show that TMWE outperforms the top system with hand-crafted features, and the current best neural network model.

Microblog sentiment classification is an interesting and important research topic with wide applications. Traditional microblog sentiment classification methods usually use a single model to classify the messages from different users and omit individuality. However, microblogging users frequently embed their personal character, opinion bias and language habits into their messages, and the same word may convey different sentiments in messages posted by different users. In this paper, we propose a personalized approach for microblog sentiment classification. In our approach, each user has a personalized sentiment classifier, which is decomposed into two components, a global one and a user-specific one. Our approach can capture the individual personality and at the same time leverage the common sentiment knowledge shared by all users. The personalized sentiment classifiers of massive users are trained in a collaborative way based on multi-task learning to handle the data sparseness problem. In addition, we incorporate users' social relations into our model to strengthen the learning of the personalized models. Moreover, we propose a distributed optimization algorithm to solve our model in parallel. Experiments on two real-world microblog sentiment datasets validate that our approach can improve microblog sentiment classification accuracy effectively and efficiently.

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by the advances in the method of moments. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. We propose GMM-LDA, a topic modeling based Bayesian unsupervised model, to analyze the document intent structure cooperated with order information. Our model is flexible that has the ability to combine the annotations and do supervised learning. Additionally, entropic regularization can be introduced to model the significant divergence between topics and intents. We perform experiments in both unsupervised and supervised settings, results show the superiority of our model over several state-of-the-art baselines.

Word embeddings, which can better capture the fine-grained semantics of words, have proven to be useful for a variety of natural language processing tasks. However, because discourse structures describe the relationships between segments of discourse, word embeddings cannot be directly integrated to perform the task. In this paper, we introduce a mixed generative-discriminative framework, in which we use vector offsets between embeddings of words to represent the semantic relations between text segments and Fisher kernel framework to convert a variable number of vector offsets into a fixed length vector. In order to incorporate the weights of these offsets into the vector, we also propose the Weighted Fisher Vector. Experimental results on two different datasets show that the proposed method without using manually designed features can achieve better performance on recognizing the discourse level relations in most cases.