TextMood is a simple and powerful sentiment analyzer, provided as a Ruby gem with a command-line tool for simple interoperability with other processes. It takes text as input and returns a sentiment score.
The sentiment analysis is relatively simple, and works by splitting the text into tokens and comparing each token to a pre-selected sentiment score for that token. The combined score for all tokens is then returned.
However, TextMood also supports doing multiple passes over the text, splitting it into tokens of N words (N-grams) for each pass. By adding multi-word tokens to the sentiment file and using this feature, you can achieve much greater accuracy than with just single-word analysis.
Please see the Contribute section for more info on how to improve the quality of these files, or adding new ones.
The easiest way to get the latest stable version is to install the gem:
gem install textmood
If you’d like to get the bleeding-edge version:
git clone https://github.com/stiang/textmood
The master branch will normally be in sync with the gem, but there may be newer code in branches.
TextMood can be used as a Ruby library or as a standalone CLI tool.
You can use it in a Ruby program like this:
require "textmood"
# The :language parameter makes TextMood use one of the bundled language files
tm = TextMood.new(language: "en")
score = tm.analyze("some text")
#=> '1.121'
# The :files parameter makes TextMood ignore the bundled sentiment files and use the
# specified files instead. You can specify as many files as you want.
tm = TextMood.new(files: ["en_US-mod1.txt", "emoticons.txt"])
# Use :alias_file to make TextMood look up the file to use for the given language tag
# in a JSON file containing a hash with {"language_tag": "path_to_file"} mappings
tm = TextMood.new(language: "zw", alias_file: "my-custom-languages.json")
# :normalize_score will try to normalize the score to an integer between +/- 100,
# based on how many tokens were scored, which can be useful when trying to compare
# scores for texts of different length
tm = TextMood.new(language: "en", normalize_score: true)
score = tm.analyze("some text")
#=> '14'
# :ternary_output will make TextMood return one of three fixed values:
# 1 for positive, 0 for neutral and -1 for negative
tm = TextMood.new(language: "en", ternary_output: true)
score = tm.analyze("some text")
#=> '1'
# :min_threshold and :max_threshold lets you customize the way :ternary_output
# treats different values. The options below will make all scores below 10 negative,
# 10-20 will be neutral, and above 20 will be positive. Note that these thresholds
# are compared to the normalized score, if applicable.
tm = TextMood.new(language: "en",
ternary_output: true,
normalize_score: true,
min_threshold: 10,
max_threshold: 20)
score = tm.analyze("some text")
#=> '0'
# TextMood will by default make one pass over the text, checking every word, but it
# supports doing several passes for any range of word N-grams. Both the start and end
# N-gram can be specified using the :start_ngram and :end_ngram options
tm = TextMood.new(language: "en", debug: true, start_ngram: 2, end_ngram: 3)
score = tm.analyze("some long text with many words")
#(stdout): some long: 0.1
#(stdout): long text: 0.1
#(stdout): text with: -0.1
#(stdout): with many: -0.1
#(stdout): many words: -0.1
#(stdout): some long text: -0.1
#(stdout): long text with: 0.1
#(stdout): text with many: 0.1
#(stdout): with many words: 0.1
#=> '0.1'
# :debug prints out all tokens to stdout, alongs with their values (or 'nil' when the
# token was not found)
tm = TextMood.new(language: "en", debug: true)
score = tm.analyze("some text")
#(stdout): some: 0.1
#(stdout): text: 0.1
#(stdout): some text: -0.1
#=> '0.1'
tm = TextMood.new(language: "en", verbose: true) score = tm.analyze("some slightly longer text that contains a few more tokens") #(stdout): Combined score: 1.0 (5 tokens, 0.2 avg.) #(stdout): Negative score: -0.5 (1 tokens, -0.5 avg.) #(stdout): Positive score: 1.5 (4 tokens, 0.375 avg.) #(stdout): Neutral score: 0.0 (0 tokens) #(stdout): Not found: 5 tokens #=> '1.0'
#### CLI tool
You can also pass some UTF-8-encoded text to the CLI tool and get a score back, like so:
```bash
textmood -l en "<some text>"
-0.4375
Alternatively, you can pipe text to textmood on stdin:
echo "<some text>" | textmood -l en
-0.4375
The cli tool has many useful options, mostly mirroring those of the library. Here’s the
output from textmood -h:
Usage: textmood [options] "<text>"
OR
echo "<text>" | textmood [options]"
Returns a sentiment score of the provided text. Above 0 is usually
considered positive, below is considered negative.
MANDATORY options:
-l, --language LANGUAGE The IETF language tag for the provided text.
Examples: en_US, no_NB
OR
-f, --file PATH TO FILE Use the specified sentiment file. May be used
multiple times to load several files. No other
files will be loaded if this option is used.
OPTIONAL options:
-a, --alias-file PATH TO FILE JSON file containing a hash that maps language codes to
sentiment score files. This lets you use the convenience of
language codes with custom sentiment score files.
-n, --normalize-score Tries to normalize the score to an integer between +/- 100
according to the number of tokens that were scored, making
it more feasible to compare scores for texts of different
length
-t, --ternary-output Return 1 (positive), -1 (negative) or 0 (neutral)
instead of the actual score. See also --min-threshold
and --max-threshold.
-i, --min-threshold FLOAT Scores lower than this are considered negative when
using --ternary-output (default 0.5). Note that the
threshold is compared to the normalized score, if applicable
-x, --max-threshold FLOAT Scores higher than this are considered positive when
using --ternary-output (default 0.5). Note that the
threshold is compared to the normalized score, if applicable
-s, --start-ngram INTEGER The lowest word N-gram number to split the text into
(default 1). Note that this only makes sense if the
sentiment file has tokens of similar N-gram length
-e, --end-ngram INTEGER The highest word N-gram number to to split the text into
(default 1). Note that this only makes sense if the
sentiment file has tokens of similar N-gram length
-k, --skip-symbols Do not include symbols file (emoticons etc.). Only applies
when using -l/--language.
-c, --config PATH TO FILE Use the specified config file. If not specified, textmood will
look for /etc/textmood.cfg and ~/.textmood. Settings in the user
config will override settings from the global file.
-d, --debug Prints out the score for each token in the provided text
or 'nil' if the token was not found in the sentiment file
-v, --verbose Prints out some useful statistics about the analysis
(counts, averages etc).
-h, --help Show this message
The CLI tool will look for /etc/textmood and ~/.textmood unless the -c/--config option is used, in which case only that file is used. The configuration files are basic, flat YAML files that use the same keys as the library understands:
# Assume that text is in this language, unless overridden on the command line.
# Do not use this in conjunction with the files setting.
language: en
# Load these sentiment score files instead of using any of the bundled ones
# Do not use this in conjunction with the language setting
files: [/path/to/file1, /path/to/file2]
# Use a global alias file to resolve language codes
alias_file: /home/john/textmood/aliases.json
# Always normalize the score
normalize_score: true
# Use ternary output
ternary_output: true
# Use these thresholds when using ternary output
max_threshold: 10
min_threshold: 5
# Do three passes, scoring unigrams, bigrams and trigrams
start_ngram: 1
end_ngram: 3
# Do not load the symbols file when using a bundled language
skip_symbols: true
# Always print debug info
debug: true
The included sentiment files reside in the lang directory. I hope to add many more baseline sentiment files in the future.
Sentiment files should be named according to the IETF language tag, like en or no_NB, and contain one colon-separated line per token, like so:
1.0: epic
1.0: good
1.0: upright
0.958: fortunate
0.875: wonderfulness
0.875: wonderful
0.875: wide-eyed
0.875: wholesomeness
0.875: well-to-do
0.875: well-situated
0.6: well suited
-0.3: dishonest
-0.5: tragedy
The score, which must be between -1.0 and 1.0, is to the left of the first ':', and everything to the right is the (potentially multi-word) token.
Including baseline word/N-gram scores for many different languages is one of the expressed goals of this project. If you are able to contribute scores for a missing language or improve an existing one, it would be much appreciated!
The process is the usual:
Loosely based on https://github.com/cmaclell/Basic-Tweet-Sentiment-Analyzer
Stian Grytøyr
FAQs
Unknown package
We found that textmood demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Ransomware payment rates hit an all-time low in 2024 as law enforcement crackdowns, stronger defenses, and shifting policies make attacks riskier and less profitable.
Security News
require(esm) backported to Node.js 20, easing the transition to ESM-only packages and reducing complexity for developers as Node 18 nears end-of-life.
Security News
PyPI now supports iOS and Android wheels, making it easier for Python developers to distribute mobile packages.