Sentiment Analysis In A Gist

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly.

Word Embedding

In linguistics, word embeddings were discussed in the research area of distributional semantics. It aims to quantify and categorize semantic similarities between linguistic items based on their distributional properties in large samples of language data. The underlying idea that "a word is characterized by the company it keeps" was popularized by John Rupert Firth
In natural language processing (NLP), word embedding is a term used for the representation of words for text analysis, typically in the form of a real-valued vector that encodes the meaning of the word such that the words that are closer in the vector space are expected to be similar in meaning. Word embeddings can be obtained using a set of language modeling and feature learning techniques where words or phrases from the vocabulary are mapped to vectors of real numbers. Conceptually it involves the mathematical embedding from space with many dimensions per word to a continuous vector space with a much lower dimension.
Methods to generate this mapping include neural networks, dimensionality reduction on the word co-occurrence matrix, probabilistic models, explainable knowledge base method,and explicit representation in terms of the context in which words appear.
Word and phrase embeddings, when used as the underlying input representation, have been shown to boost the performance in NLP tasks such as syntactic parsing and sentiment analysis.

Lexical Normalization

Lexical normalization is the task of translating/transforming a non standard text to a standard register.

Datasets usually consists of tweets, since these naturally contain a fair amount of these phenomena.

For lexical normalization, only replacements on the word-level are annotated. Some corpora include annotation for 1-N and N-1 replacements. However, word insertion/deletion and reordering is not part of the task.

LexNorm

The LexNorm corpus was originally introduced by Han and Baldwin (2011). Several mistakes in annotation were resolved by Yang and Eisenstein; on this page, we only report results on the new dataset. For this dataset, the 2,577 tweets from Li and Liu(2014) is often used as training data, because of its similar annotation style.

This dataset is commonly evaluated with accuracy on the non-standard words. This means that the system knows in advance which words are in need of normalization.

Stop Words

Natural Language Processing with PythonNatural language processing (nlp) is a research field that presents many challenges such as natural language understanding.

Text may contain stop words like ‘the’, ‘is’, ‘are’. Stop words can be filtered from the text to be processed. There is no universal list of stop words in nlp research, however the nltk module contains a list of stop words.

We need a way to convert words to values, in numbers, or signal patterns. The process of converting data to something a computer can understand is referred to as "pre-processing." One of the major forms of pre-processing is going to be filtering out useless data. In natural language processing, useless words (data), are referred to as stop words.

Levenshtein Distance

The Levenshtein distance is a string metric for measuring difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (i.e. insertions, deletions or substitutions) required to change one word into the other. It is named after Vladimir Levenshtein, who considered this distance in 1965.

Levenshtein distance may also be referred to as edit distance, although it may also denote a larger family of distance metrics. It is closely related to pairwise string alignments.

Dynamic Programming Approach

The Levenshtein algorithm calculates the least number of edit operations that are necessary to modify one string to obtain another string. The most common way of calculating this is by the dynamic programming approach:

1. A matrix is initialized measuring in the (m, n) cell the Levenshtein distance between the m-character prefix of one with the n-prefix of the other word.
2. The matrix can be filled from the upper left to the lower right corner.
3. Each jump horizontally or vertically corresponds to an insert or a delete, respectively.
4. The cost is normally set to 1 for each of the operations.
5. The diagonal jump can cost either one, if the two characters in the row and column do not match else 0, if they match. Each cell always minimizes the cost locally.
6. This way the number in the lower right corner is the Levenshtein distance between both words.

An example that features the comparison of “HONDA” and “HYUNDAI”.

Text-Unstructured Data?

Unstructured text is very common and, in fact, may represent the majority of information available to a particular research or data mining project. The selection of tools or techniques available with STATISTICA, along with the Text Mining module, can help organizations to solve a variety of problems. A few to mention are the following:

1

Extracting information reflecting customers/employees/public—opinions, needs, and interest (e.g., visualizing semantic spaces using 2D, 3D plots);

2

Filtering unwanted documents/emails (using stop list, include lists, etc.);

3

Predicting customer satisfaction levels (e.g., negative connotations);

4

Clustering similar words/documents. (e.g., reviews, research papers, survey data, etc.);

5

Classifying or organizing documents (e.g., electronic documents about general information can be classified into different subgroups);

6

Predicting/routing new documents, etc. (The rules for clustering or classifying or predicting can be used to score new documents.)