Building a Twitter Sentiment Analysis System with Recurrent Neural Networks

This paper presents a sentiment analysis solution on tweets using Recurrent Neural Networks (RNNs). The method is can classifying tweets with an 80.74% accuracy rate, considering a binary task, after experimenting with 20 different design approaches. The solution integrates an attention mechanism aiming to enhance the network, with a two-way localization system: at memory cell level and at network level. We present an in-depth literature review for Twitter sentiment analysis and the building blocks that grounded the design decisions of our solution, employed as a core classification component within a sentiment indicator of the SynergyCrowds platform.

Institutional narratives of the shift to media literacy: Interviewing New Zealand’s Office of Film and Literature Classification

This article presents a case study of media literacy deployment in contemporary media regulation through interviews with New Zealand’s Chief Censor and other key staff members in the Office of Film and Literature Classification. The interviews offer an account of how media literacy came to be adopted as a prominent strategy within their policy remit. This article explores how the institutional narrative reconciles their media literacy approaches and core classification work, finding four key discursive and practical strategies: integrating, expanding, reconceptualizing and reasserting relevance. The analysis also reveals how the standpoint of media as a site of potential harm shapes the meaning of media literacy in this institutional context. This case study provides insight into the utility of media literacy for contemporary media regulation, examining how a classification agency navigates change and continuity in the face of a challenging and rapidly evolving media landscape.

Comprehensive correction method of airtight coring saturation based on core classification

The indoor comprehensive analysis of core saturation of airtight coring wells is an important part of well logging interpretation. According to the saturation data, the geological reserves can be accurately calculated, and the remaining oil saturation and water-flooded zone in the later stage of the production well can be accurately evaluated. Due to the influence of many factors in the coring process and the experiment process, the sum of the core oil and water saturation is usually not equal to 100%. At present, conventional airtight coring correction method is generally to analyze the oil-water saturation, and then correct the data of the same factors that affect the results. This article combines two methods for saturation correction of XX oilfield in China. For cores with consistent missing factors, mathematical statistics are used to correct the saturation. When most of the rock pores have irreducible water and remaining oil, the phase percolation split method is used for the correction after the experimental analysis. By comparing with the logging interpretation results and the results of adjacent wells, the feasibility of the comprehensive correction method can be verified.

Maintaining Records in Context? Disrupting the Theory and Practice of Archival Classification and Arrangement

ABSTRACT The role and the associated practices of the archivist are attuned to notions of facilitation. Archivists facilitate people's engagement with the historical record by providing access to records in context: a context instantiated through archival classification, arrangement, and description. In the second of a two-part article, the author draws from the archival literature to present a historical overview of the factors that contributed to evolving notions of archival classification and arrangement from the 1960s to today. A review of the literature of this time frame provides its own context for understanding how, why, and through whose influence competing understandings and implementations of core classification ideas persist. In the process, the author highlights classification as a historically situated interpretive act, drawing attention to the implications of various disciplinary influences and analytical perspectives on the present status and future conception of, and possibilities for, the American archival profession.

Construction of Decision Tree Based on Rough Sets Theory

In the process of constructing decision trees, the selecting criteria of classification attributes will directly affect the classification results. Here we presented the classification contribution function (CCF), a new concept based on rough sets theory, which is regarded as the criteria for choosing attributes in the core of attributes. The basic idea of CCF is using of discernibility matrix to determine attribute core (if there is no core, using its reduction). Then through the classification contribution function to determine core classification contribution value and employ the big value as node. Next employing the selected attribute ways to divide decision-making system and each value attribute can produce a subset. The experiments show that, being compared with the entropy based C4.5 and weighted mean roughness, our method can get simpler decision tree and improve the efficiency of classification.