Unsupervised sentiment classification usually needs a user defined sentiment dictionary. However, the existing dictionaries in Chinese are insufficient, for example, the intersection rate of two popular Chinese sentiment dictionaries HowNet and NTUSD is less than 10%. In this paper, we present a method to help expand the dictionaries with more sentiment words by ranking them through link analys...

Automated sentiment polarity prediction from text is a challenging problem addressed in this paper. We present results of a comparison of pure “Bag of Words” approach against a mixed method, extended by detecting opinion patterns using shallow-parsing techniques. We utilize two resources for the analysis: Spejd shallow parsing engine and Zetema dictionary of sentiment in Polish. The performance...

While microblogging has gained popularity on the Internet, analyzing and processing short messages has become a challenging task in natural language processing. This paper analyzes the differences between Internet short messages (or “microtext”) and general articles by comparing the Plurk Corpus and the Sinica Balanced Corpus. Likelihood ratio and the tóngyìcícílín (“ ”) thesaurus are adopted t...

This paper investigates the role played by social media platform Reddit in events around GameStop (GME) share rally early 2021. In particular, we analyze impact of discussions on r/WallStreetBets subreddit price dynamics American online retailer GameStop. We customize a sentiment analysis dictionary for users based Valence Aware Dictionary and Sentiment Reasoner (VADER) package perform textual ...

Sentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. Ideally, an opinion mining tool would process a set of search results for a given item, generating a list of product attributes (quality, features, etc.) and aggregating opinions about each of them (poor, mixed, good). Then begin by ident...

This paper introduces the augmented NTU sentiment dictionary, abbreviated as ANTUSD, which is constructed by collecting sentiment stats of words in several sentiment annotation work. A total of 26,021 words were collected in ANTUSD. For each word, the CopeOpi numerical sentiment score and the number of positive annotation, neutral annotation, negative annotation, non-opinionated annotation, and...

Polarity classification of words is important for applications such as Opinion Mining and Sentiment Analysis. A number of sentiment word/sense dictionaries have been manually or (semi)automatically constructed. The dictionaries have substantial inaccuracies. Besides obvious instances, where the same word appears with different polarities in different dictionaries, the dictionaries exhibit compl...

Opinion Mining and Sentiment Analysis is a process of identifying opinions in large unstructured/structured data and then analysing polarity of those opinions. Opinion mining and sentiment analysis have found vast application in analysing online ratings, analysing product based reviews, egovernance, and managing hostile content over the internet. This paper proposes an algorithm to implement as...

---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – Sentiment Analysis is the process of determining whether a piece of writing is positive, negative or neutral. It’s also known as opinion mining, deriving the opinion or attitude of a speaker. Sentiment analysis is the process which helps in ident...

We examined the validity of 37 sentiment scores based on dictionary-based methods using a large news corpus and demonstrated risk generating spectrum results with different levels statistical significance by presenting an analysis relationships between U.S. presidential approval. summarize our findings into four best practices: 1) use suitable dictionary; 2) do not assume that reliability dicti...