Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

Clustering Product Features for Opinion Mining

In sentiment analysis of product reviews, one important problem is to produce a summary of opinions based on product features/attributes (also called aspects). However, for the same feature, people can express it with many different words or phrases. To produce a useful summary, these words and phrases, which are domain synonyms, need to be grouped under the same feature group. Although several methods have been proposed to extract product features from reviews, limited work has been done on clustering or grouping of synonym features. This paper focuses on this task. Classic methods for solving this problem are based on unsupervised learning using some forms of distributional similarity. However, we found that these methods do not do well. We then model it as a semi-supervised learning problem. Lexical characteristics of the problem are exploited to automatically identify some labeled examples. Empirical evaluation shows that the proposed method outperforms existing state-of-the-art methods by a large margin

Identifying Evaluative Sentences in Online Discussions

Much of opinion mining research focuses on product reviews because reviews are opinion-rich and contain little irrelevant information. However, this cannot be said about online discussions and comments. In such postings, the discussions can get highly emotional and heated with many emotional statements, and even personal attacks. As a result, many of the postings and sentences do not express positive or negative opinions about the topic being discussed. To find people’s opinions on a topic and its different aspects, which we call evaluative opinions, those irrelevant sentences should be removed. The goal of this research is to identify evaluative opinion sentences. A novel unsupervised approach is proposed to solve the problem, and our experimental results show that it performs well. 1

Petrogenesis of the Neoarchean diorite-granite association in the Wangwushan area, southern North China Craton: Implications for continental crust evolution

The crustal evolutionary scenarios and geodynamic driving mechanisms of the North China Craton (NCC) during the late Neoarchean (similar to 2.5 Ga) are still lacking comprehensive understanding due to subsequent strong deformation and metamorphic overprinting events. The widespread similar to 2.5 Ga tectono-thermal activities throughout the NCC can be studied to constrain the tectonic evolution at this period. The Wangwushan Neoarchean diorites and high-K granites in the southern NCC were formed at similar to 2.52-2.51 Ga. The diorites have high Mg#, Th, Y, low Sr and Sr/Y ratios and variably positive zircon epsilon(Hf)(t) (+2.4 - +8.4) and whole rock epsilon(Nd)(t) values (-0.26 - +3.24), indicating a depleted mantle wedge source which was metasomatized by the melted sub-ducted silicic sediments. The depleted Nb, Ti and enriched LILE and Pb features indicate that the diorites were derived from slightly metasomatized mantle wedge in a subduction-related setting. The high-K granites show a shoshonite affinity and peraluminous features. The rocks have low 10,000 Ga/Al ratios, Zr and Zr + Nb + Ce + Y concentrations, and calculated zircon saturation temperature (T-Zr = 686-837 degrees C, 774 degrees C on average), belong to fractionated I-type granite. Low Sr, Y, flat HREE, and negative Eu anomalies indicate the presence of Ca-rich plagioclase and absence of garnet in the residue during partial melting of the sources, thus further indicating a shallower source with a pressure of < 10 kbar and a depth less than 35 km. Zircon Hf and whole-rock Nd isotopic compositions (epsilon(Hf)(t) = -2.7 to +7.4, epsilon(Nd)(t) = +1.25 to +3.09, T-DM2 ages are 3.20-2.56 Ga and 3.20-2.65 Ga, respectively) are identical to those of the Neoarchean TTGs, amphibolites and diorites in the Wangwushan area, which likely represent plausible sources for the high-K granites. The partial melting was probably triggered by underplating of mantle-derived mafic magmas in a post-collision setting. Together with 2.57-2.52 Ga TTGs and amphibolites in this area, the magmatic spectrum of these igneous rocks are typical of subduction-related magmatism, involving multi-stage processes in a convergent plate margin, and likely record a transitional regime from early oceanic plate subduction to late post-collisional extension