While sentiment dictionaries are easy to apply and provide reproducible results, they often exhibit inferior classification performance compared machine learning approaches trained for specific application domains. Nevertheless, both typically require manual data analysis. This paper develops a domain-specific dictionary using regularised linear models drawing from textual reports of financial ...

Sentiment analysis is the area of Natural Language Processing that aims to determine the polarity (positive, negative, neutral) contained in an opinionated text. A usual resource employed in many of these approaches are the so-called polarity lexicons. A polarity lexicon acts as a dictionary that assigns a sentiment polarity value to words. In this work we explore the possibility of automatical...

In recent years, the Web and social media are growing exponentially. We are provided with documents which have opinions expressed about several topics. This constitute a rich source for Natural Language Processing tasks, in particular, Sentiment Analysis. In this work, we aim at constructing a sentiment dictionary based on words obtained from web pages related to a specific domain. To do so, we...

This paper introduces Czech subjectivity lexicon – the new lexical resource for sentiment analysis in Czech. The lexicon is a dictionary of 4947 evaluative items annotated with part of speech and tagged with positive or negative polarity. We describe the method for building the basic vocabulary and the criteria for its manual refinement. Also, we suggest possible enrichment of the fundamental l...

In this paper we propose a method that uses corpora where phrases are annotated as Positive, Negative, Objective and Neutral, to achieve new sentiment resources involving words dictionaries with their associated polarity. Our method was created to build sentiment words inventories based on sentisemantic evidences obtained after exploring text with annotated sentiment polarity information. Throu...

The discipline where sentiment/opinion/emotion has been identified and classified in human written text is well known as sentiment analysis. A typical computational approach to sentiment analysis starts with prior polarity lexicons where entries are tagged with their prior out of context polarity as human beings perceive using cognitive knowledge. Till date, all research efforts found in sentim...

Correlate tweets with weather at corresponding location Mathematica’s WeatherData package Uses National Weather Service data For each of the 20 metropolitan areas, every hour: Cloud cover, Humidity, Precipitation, Temperature ,Wind speed Data Most methods for sentiment analysis use scored word lists Affective Norms of English Words (ANEW) Wilson, Wiebe and Hoffmann (WWH) Hu and Liu (HL) Can we ...

The aim of this paper is to introduce the Czech subjectivity lexicon, a new lexical resource for sentiment analysis in Czech. We describe particular stages of the manual refinement of the lexicon and demonstrate its use in the state-of-the art polarity classifiers, namely the Maximum Entropy classifier. We test the success rate of the system enriched with the dictionary on different data sets, ...

Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, — a “sentiment lexicon” or “affective word lists”. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) deve...