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The Sentiment Analysis is an application of Natural Language Processing which targets on the identification of the sentiment (positive vs negative vs neutral), the subjectivity (objective vs subjective) and the emotional states of the document.
I worked on the particular project for over 9 months and used several different statistical methods and techniques under the supervision of Professors Tsiamyrtzis and Kakadiaris.
During my thesis, I had the opportunity learn about new machine learning techniques but also bumped into some interesting and non-obvious matters.
In this article I discuss the things that I found most interesting while working on the Sentiment Analysis project and I provide some tips that you should have in mind while working on similar Natural Language Processing problems.
This technique uses dictionaries of words annotated with their semantic orientation (polarity and strength) and calculates a score for the polarity of the document.
This means that you must first gather a dataset with examples for positive, negative and neutral classes, extract the features/words from the examples and then train the algorithm based on the examples.
On the other hand Learning based techniques deliver good results nevertheless they require obtaining datasets and require training.
Syntactic techniques can deliver better accuracy because they make use of the syntactic rules of the language in order to detect the verbs, adjectives and nouns.
Unfortunately such techniques heavily depend on the language of the document and as a result the classifiers can’t be ported to other languages.
Statistical techniques have 2 significant benefits over the Syntactic ones: we can use them in other languages with minor or no adaptations and we can use Machine Translation of the original dataset and still get quite good results.
Training the classifier to detect only the 2 classes forces several neutral words to be classified either as positive or negative something that leads to over fitting.
Usually Binarized versions (occurrences clipped to 1) of the algorithms perform better than the ones that use multiple occurrences.
For example you might find that Max Entropy with Chi-square as feature selection is the best combination for restaurant reviews, while for twitter the Binarized Naïve Bayes with Mutual Information feature selection outperforms even the SVMs.
Particularly in case of twitter, avoid using lexicon based techniques because users are known to use idioms, jargons and twitter slangs what heavily affect the polarity of the tweet.
Ask yourself if it delivers the results that you expect or if it makes your algorithm unnecessary complicated and difficult to explain its results.
One of the most powerful techniques for building highly accurate classifiers is using ensemble learning and combining the results of different classifiers.
Opinion mining (sometimes known as sentiment analysis or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Precursors to sentimental analysis include the General Inquirer, which provided hints toward quantifying patterns in text and, separately, psychological research that examined a person's psychological state based on analysis of their verbal behavior. Subsequently, the method described in a patent by Volcani and Fogel, looked specifically at sentiment and identified individual words and phrases in text with respect to different emotional scales.
One can also classify a document's polarity on a multi-way scale, which was attempted by Pang and Snyder among others: Pang and Lee expanded the basic task of classifying a movie review as either positive or negative to predict star ratings on either a 3- or a 4-star scale, while Snyder performed an in-depth analysis of restaurant reviews, predicting ratings for various aspects of the given restaurant, such as the food and atmosphere (on a five-star scale).
— were taken in the 2004 AAAI Spring Symposium where linguists, computer scientists, and other interested researchers first aligned interests and proposed shared tasks and benchmark data sets for the systematic computational research on affect, appeal, subjectivity, and sentiment in text. Even though in most statistical classification methods, the neutral class is ignored under the assumption that neutral texts lie near the boundary of the binary classifier, several researchers suggest that, as in every polarity problem, three categories must be identified.
When a piece of unstructured text is analyzed using natural language processing, each concept in the specified environment is given a score based on the way sentiment words relate to the concept and its associated score. This allows movement to a more sophisticated understanding of sentiment, because it is now possible to adjust the sentiment value of a concept relative to modifications that may surround it.
Alternatively, texts can be given a positive and negative sentiment strength score if the goal is to determine the sentiment in a text rather than the overall polarity and strength of the text. This task is commonly defined as classifying a given text (usually a sentence) into one of two classes: objective or subjective. This problem can sometimes be more difficult than polarity classification. The subjectivity of words and phrases may depend on their context and an objective document may contain subjective sentences (e.g., a news article quoting people's opinions).
Different features can generate different sentiment responses, for example a hotel can have a convenient location, but mediocre food. This problem involves several sub-problems, e.g., identifying relevant entities, extracting their features/aspects, and determining whether an opinion expressed on each feature/aspect is positive, negative or neutral. The automatic identification of features can be performed with syntactic methods, with topic modeling, or with deep learning. More detailed discussions about this level of sentiment analysis can be found in Liu's work. Existing approaches to sentiment analysis can be grouped into three main categories: knowledge-based techniques, statistical methods, and hybrid approaches. Knowledge-based techniques classify text by affect categories based on the presence of unambiguous affect words such as happy, sad, afraid, and bored. Some knowledge bases not only list obvious affect words, but also assign arbitrary words a probable 'affinity' to particular emotions. Statistical methods leverage on elements from machine learning such as latent semantic analysis, support vector machines, 'bag of words' and 'Semantic Orientation — Pointwise Mutual Information'.
Grammatical dependency relations are obtained by deep parsing of the text. Hybrid approaches leverage on both machine learning and elements from knowledge representation such as ontologies and semantic networks in order to detect semantics that are expressed in a subtle manner, e.g., through the analysis of concepts that do not explicitly convey relevant information, but which are implicitly linked to other concepts that do so. Open source software tools deploy machine learning, statistics, and natural language processing techniques to automate sentiment analysis on large collections of texts, including web pages, online news, internet discussion groups, online reviews, web blogs, and social media. Knowledge-based systems, on the other hand, make use of publicly available resources, to extract the semantic and affective information associated with natural language concepts.
To address this issue a number of rule-based and reasoning-based approaches have been applied to sentiment analysis, including defeasible logic programming. Also, there is a number of tree traversal rules applied to syntactic parse tree to extract the topicality of sentiment in open domain setting. The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments.
As businesses look to automate the process of filtering out the noise, understanding the conversations, identifying the relevant content and actioning it appropriately, many are now looking to the field of sentiment analysis. Further complicating the matter, is the rise of anonymous social media platforms such as 4chan and Reddit. If web 2.0 was all about democratizing publishing, then the next stage of the web may well be based on democratizing data mining of all the content that is getting published. One step towards this aim is accomplished in research.
Tweets' political sentiment demonstrates close correspondence to parties' and politicians' political positions, indicating that the content of Twitter messages plausibly reflects the offline political landscape. Furthermore, sentiment analysis on Twitter has also been shown to capture the public mood behind human reproduction cycles on a planetary scale[peacock term], as well as other problems of public-health relevance such as adverse drug reactions.
Enhancing deep learning sentiment analysis with ensemble techniques in social applications
They provide automatic feature extraction and both richer representation capabilities and better performance than traditional feature based techniques (i.e., surface methods).
Traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods.
In this paper we seek to improve the performance of deep learning techniques integrating them with traditional surface approaches based on manually extracted features.
First, we develop a deep learning based sentiment classifier using a word embeddings model and a linear machine learning algorithm.
7 Applications of Deep Learning for Natural Language Processing
The field of natural language processing is shifting from statistical methods to neural network methods.
it is the fact that a single model can learn word meaning and perform language tasks, obviating the need for a pipeline of specialized and hand-crafted methods.
have tried to focus on the types of end-user problems that you may be interested in, as opposed to more academic or linguistic sub-problems where deep learning does well such as part-of-speech tagging, chunking, named entity recognition, and so on.
For more on the general topic, see: Below are 3 examples of deep learning papers for text classification: Language modeling is really a subtask of more interesting natural language problems, specifically those that condition the language model on some other input.
The task of speech recognition is to map an acoustic signal containing a spoken natural language utterance into the corresponding sequence of words intended by the speaker.
In a machine translation task, the input already consists of a sequence of symbols in some language, and the computer program must convert this int a sequence of symbols in another language.
Some examples include: For more on neural machine translation, see: Below are 3 examples of deep learning for machine translation: Document summarization is the task where a short description of a text document is created.
Some examples of document summarization include: For more on the topic, see: Below are 3 examples of deep learning for document summarization: Question answering is the problem where given a subject, such as a document of text, answer a specific question about the subject.
question answering systems which try to answer a user query that is formulated in the form of a question by return the appropriate none phrase such as a location, a person, or a date.
Some examples include: For more information on question answering, see: Below are 3 examples of deep learning for question answering: This section provides more resources on deep learning applications for NLP if you are looking go deeper.
Introduction to Natural Language Processing (NLP)
“Natural Language Processing is a field that covers computer understanding and manipulation of human language, and it’s ripe with possibilities for newsgathering,” Anthony Pesce said in Natural Language Processing in the kitchen. “You usually hear about it in the context of analyzing large pools of legislation or other document sets, attempting to discover patterns or root out corruption.”
By utilizing NLP, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
“Apart from common word processor operations that treat text like a mere sequence of symbols, NLP considers the hierarchical structure of language: several words make a phrase, several phrases make a sentence and, ultimately, sentences convey ideas,” John Rehling, an NLP expert at Meltwater Group, said in How Natural Language Processing Helps Uncover Social Media Sentiment.
“By analyzing language for its meaning, NLP systems have long filled useful roles, such as correcting grammar, converting speech to text and automatically translating between languages.” NLP is used to analyze text, allowing machines to understand how human’s speak.
Publishers are hoping to use NLP to improve the quality of their online communities by leveraging technology to “auto-filter the offensive comments on news sites to save moderators from what can be an ‘exhausting process’,” Francis Tseng said in Prototype winner using ‘natural language processing’ to solve journalism’s commenting problem.
Use NLP to build your own RSS reader You can build a machine learning RSS reader in less than 30-minutes using the follow algorithms: If you’re interested in learning more, this free introductory course from Stanford University will help you will learn the fundamentals of natural language processing, and how you can use it to solve practical problems.
- On Monday, March 18, 2019
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