Hello,

Maybe this discussion is outdated and you already found a good solution for your problem, but for those still interested in the topic, maybe you'll want to consider syuzhet.

Relies on four (4) dictionaries and basically addresses the immediate needs for sentiment analysis and is up to date.

Cheers!

More info:

https://cran.r-project.org/web/packages/syuzhet/index.html

you could see the qdap package (function polarity)

Hi, also you can have a look at the tm package.

Here some help:

tm:

http://www.rdatamining.com/examples/text-mining

qdap:

http://www.r-bloggers.com/statistics-meets-rhetoric-a-text-analysis-of-i-have-a-dream-in-r/

Good luck

You can also try using the older archived package called "Sentiment" which can be downloaded from here: http://cran.r-project.org/src/contrib/Archive/sentiment/.

There's also the ViralHeat Sentiment API which can be used with R and R Studio: https://www.viralheat.com/data-apis/.

Happy computing!

There are various APIs for sentiment analysis you can use, see e.g. a list of APIs available on Mashape http://blog.mashape.com/list-of-20-sentiment-analysis-apis/ Some of them are free, and some have a certain free usage allowance.

You'd only need to write a client in R to interact with such a service. It is quite straightforward with the httr library (http://cran.r-project.org/web/packages/httr/index.html).

Dear Mr Roseric, in addition to the above solutions exposed, in my humble opinion you could take a look to Weka, there is considerable literature on how to use it for sentiment analysis. Another possibility may be STATISTICA (StatSoft Software), but you must pay for the license.

Best regards.

Dear all, thank you so much for your answers. I am so happy since I got so many suggestions and even solutions I never heard of before. I will check them up one by one.

THANK YOU ALL!

try using rapidminer.so many plugins available.

step by step tutorial available in the link below..

vancouverdata.blogspot.com

Rapid Miner is great for sentiment analysis and also supports R with a specific plugin. vancouverdata.blogspot.com is a good starting point to create sentiment analysis processes with RM, then it depends on what you need to do and which libraries you have to use.

https://cran.r-project.org/src/contrib/Archive/sentiment/ just download the zip file from here and extract it in the place of either the preloaded packages or the downloaded packages of R. You can see the location of these two places from : Tools->Global options.

Has anyone used quanteda https://github.com/kbenoit/quanteda? 

I would be very interested in papers that have used this package

Salvador Ruiz de Maya, thank you so much for suggesting this package, I will give it a try.

Hello,

Maybe this discussion is outdated and you already found a good solution for your problem, but for those still interested in the topic, maybe you'll want to consider syuzhet.

Relies on four (4) dictionaries and basically addresses the immediate needs for sentiment analysis and is up to date.

Cheers!

More info:

https://cran.r-project.org/web/packages/syuzhet/index.html

Hello

You can try

https://rstudio-pubs-static.s3.amazonaws.com/31867_8236987cf0a8444e962ccd2aec46d9c3.html

In R 3.3.3 there is package named "SentimentAnalysis "

Assuming you are about to conduct a dictionary-based sentiment analysis, you may look into the following three options:

The tidytext package comes with three inbuilt, English-language dictionaries; see: https://www.tidytextmining.com/sentiment.html

If you want to supply a dictionary on your own, the dictionary() and corresponding extraction into a separate dfm() in the quanteda package may be useful: https://rdrr.io/cran/quanteda/man/dictionary.html

The most recent version of quanteda also ships with the Lexicoder Sentiment Dictionary (Young and Soroka 2011) for English political language, after loading qunateda it can be accessed by data_dictionary_LSD2015

Finally with the family of grep() functions in base R or str_count() from the stringR package you can easily write a dictionary counter on your own.

Note that dictionary-based sentiment scores might be somewhat akwardly distributed, limiting their interpretation as ratio or interval scales: Preprint Validating a sentiment dictionary for German political language

One answer that was not mentioned is using coreNLP CRAN package, which provides interface to sentiment analysis from the Java Stanford CoreNLP library.

FORMATION OF A SEMANTIC KERNEL IN VETERINARY MEDICINE WITH THE AUTOMATED SYSTEM-COGNITIVE ANALYSIS OF PASSPORTS OF SCIENTIFIC SPECIALTIES OF THE HIGHER ATTESTATION COMMISSION OF THE RUSSIAN FEDERATION AND THE AUTOMATIC CLASSIFICATION OF TEXTS ACCORDING TO THE AREAS OF SCIENCE

Lutsenko Evgeniy Veniaminovich

Dr.Sci.Econ., Cand.Tech.Sci., professor

Scopus Author ID: 57188763047

RSCI SPIN-code: 9523-7101

[email protected] http://lc.kubagro.ru

Kuban State Agrarian University, Krasnodar, Russia

This work is a continuation of the author's series of works on cognitive veterinary medicine. The present period is characterized by the appearance of huge volumes of texts in different languages in the open access, generated by people. Currently, these texts are accumulated in various electronic libraries and bibliographic databases (WoS, Scopus, RSCI, etc.), as well as on the Internet on various sites. All these texts have specific authors, dates and can belong simultaneously to many non-alternative categories and genres, in particular: educational; scientific; artistic; political; news; chats; forums and many others. The solution of the generalized problem of attribution of texts is of great scientific and practical interest, i.e. studying these texts, which would reveal their probable authors, date of creation, the ownership of these texts to the above generalized categories or genres, and might evaluate the similarities - differences of authors and texts according to their content, highlight key words etc. To solve all these problems it seems necessary to form the generalized linguistic images of texts into groups (classes), i.e. to form semantic kernels of classes. A special case of this problem is the creation of the semantic kernel in various scientific specialties of the HAC of the Russian Federation and the automatic classification of scientific texts in the areas of science. Traditionally, this task is solved by dissertation councils, i.e. experts, on the basis of expert assessments, i.e. in an informal way, on the basis of experience, intuition and professional competence. However, the traditional approach has a number of serious drawbacks that impose significant limitations on the quality and volume of analysis. Currently, there are all grounds to consider these restrictions as unacceptable, because they can be overcome. Thus, there is a problem, the solutions of which are the subject of consideration in this article. Therefore, the efforts of researchers and developers to overcome them are relevant. Therefore, the aim of the work is to develop an automated technology (method and tools), as well as methods of their application for the formation of the semantic core of veterinary medicine by automated system-cognitive analysis of passports of scientific specialties of the HAC of the Russian Federation and automatic classification of texts in the areas of science. A detailed numerical example of solving the problem on real data has been given as well

Keywords: AUTOMATED SYSTEM-COGNITIVE ANALYSIS, ASC-ANALYSIS, "EIDOS" SYSTEM

Луценко Е.В. Формирование семантического ядра ветеринарии путем Автоматизированного системно-когнитивного анализа паспортов научных специальностей ВАК РФ и автоматическая классификация текстов по направлениям науки / Е.В. Луценко // Политематический сетевой электронный научный журнал Кубанского государственного аграрного университета (Научный журнал КубГАУ) [Электронный ресурс]. – Краснодар: КубГАУ, 2018. – №10(144). С. 44 – 102. – IDA [article ID]: 1441810033. – Режим доступа: http://ej.kubagro.ru/2018/10/pdf/33.pdf, 3,688 у.п.л.

http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf

THEORETICAL FOUNDATIONS, TECHNOLOGY AND TOOLS

AUTOMATED SYSTEM-COGNITIVE ANALYSIS

(theory and method of quantitative detection in a comparable form of force and direction of cause-and-effect dependences in incomplete noisy data of very large dimension of numerical and non-numerical nature, measured in different types of scales and units of measurement)

This section is about the ask-the analysis and the system "EIDOS-X++"

There are many artificial intelligence systems. Universal cognitive analytical system "Eidos-x++" differs from them in the following parameters:

- developed in a universal setting, independent of the subject area. Therefore, it is universal and can be applied in many subject areas (http://lc.kubagro.ru/aidos/index.htm);

- is in full open free access (http://lc.kubagro.ru/aidos/_Aidos-X.htm), and with relevant source texts (http://lc.kubagro.ru/__AIDOS-X.txt);

- is one of the first domestic systems of artificial intelligence of the personal level, i.e. it does not require special training from the user in the field of artificial intelligence technologies (there is an act of introduction of the "Eidos" system of 1987) (http://lc.kubagro.ru/aidos/aidos02/PR-4.htm);

- provides stable detection in a comparable form of force and direction of cause-and-effect dependencies in incomplete noisy interdependent (nonlinear) data of very large dimension of numerical and non-numerical nature, measured in different types of scales (nominal, ordinal and numerical) and in different units of measurement (i.e. does not impose strict requirements on the data that can not be performed, and processes the data that is);

- contains a large number of local (supplied with the installation) and cloud-based educational and scientific applications (currently 31 and 135, respectively) (http://lc.kubagro.ru/aidos/Presentation_Aidos-online.pdf);

- provides multi-language interface support in 44 languages. Language databases are included in the installation and can be replenished automatically;

- supports on-line learning environment and is widely used all over the world (http://aidos.byethost5.com/map5.php);

- the most computationally intensive operations of model synthesis and recognition are realized with the help of a graphics processor (GPU), which on some tasks provides acceleration of solving these problems by several thousand times, which really provides intelligent processing of big data, big information and big knowledge;

- provides conversion of initial empirical data into information, and its knowledge and solution using this knowledge of classification problems, decision support and research of the subject area by studying its system-cognitive model, while generating a very large number of tabular and graphical output forms (development of cognitive graphics), many of which have no analogues in other systems (examples of forms can be found in: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf)

Check https://cran.r-project.org/web/views/NaturalLanguageProcessing.html

for a comprehensive view on NLP in R.

It's also worth mentioning that tidytext has an accompanying book freely available online https://www.tidytextmining.com/

H20

Python

Mahout

Weka

Rapid Miner