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# Linguistic Sentiment Analysis

The sentiment analysis grammar files contain dictionaries of types of word (for example, positive adjective, negative noun, neutral adverb, and so on), and patterns that describe how to combine these dictionaries to form positive and negative phrases.

For example, you could run sentiment extraction using the English sentiment grammar file (`sentiment_eng.ecr`), with the following hotel review as the input file:

The room was nice enough, with a plug in radiator, tv with an English news channel, hot shower, comfy bed. The receptionist we first dealt with was miserable and rude, and just grunted at us and rolled her eyes because we were too early for check in having just got off the morning train from Khabarovsk. Fortunately, a younger receptionist with a nice smile appeared, spoke to us helpfully suggesting a few cafes nearby to pass some time, and we tried to forget about the other woman.
Breakfast is terrible. Unidentifiable cordials, gloomy porridge, bread rolls filled with things you don't expect for breakfast, like potato, egg and dill. Don't come here for the breakfast, but for the cost of the room in a city like Vladivostok, the hotel is still decent value for money.

The following is a sample of the output that this produces:

    <?xml version="1.0" encoding="UTF-8"?>
      <MATCHLIST>
        <DOCUMENT Type="IDOL IDX" ID="Unknown">
          <FIELD Name="DRECONTENT">
            <FIELD_INSTANCE Value="1">
              <MATCH EntityName="sentiment/positive/eng" Offset="7" OffsetLength="5"
              Score="1.05" NormalizedTextSize="17" NormalizedTextLength="17"
              OriginalTextSize="17" OriginalTextLength="17">
                <ORIGINAL_TEXT>The room was nice</ORIGINAL_TEXT>
                <NORMALIZED_TEXT>The room was nice</NORMALIZED_TEXT>
                <COMPONENTS>
                  <COMPONENT Name="TOPIC" Text="The room" Offset="0"
                  OffsetLength="0" TextSize="8" TextLength="8"/>
                  <COMPONENT Name="SENTIMENT" Text="nice" Offset="13"
                  OffsetLength="13" TextSize="4" TextLength="4"/>
                </COMPONENTS>
              </MATCH>
              <MATCH EntityName="sentiment/negative/eng" Offset="494"
              OffsetLength="492" Score="1.2" NormalizedTextSize="21"
              NormalizedTextLength="21" OriginalTextSize="21"
              OriginalTextLength="21">
                <ORIGINAL_TEXT>Breakfast is terrible</ORIGINAL_TEXT>
                <NORMALIZED_TEXT>Breakfast is terrible</ NORMALIZED_TEXT>
                <COMPONENTS>
                  <COMPONENT Name="TOPIC" Text="Breakfast" Offset="0"
                  OffsetLength="0" TextSize="9" TextLength="9"/>
                  <COMPONENT Name="SENTIMENT" Text="terrible" Offset="13"
                  OffsetLength="13" TextSize="8" TextLength="8"/>
                </COMPONENTS>
              </MATCH>
            </FIELD_INSTANCE>
          </FIELD>
        </DOCUMENT>
    </MATCHLIST>

The following example configuration shows the recommended usage:

    [Eduction]
    ResourceFiles=grammars/sentiment_eng.ecr
    // Note: replace sentiment_eng.ecr by sentiment_user_eng.ecr if using user modification

    // standard entities for all sentiment analysis in English:
    Entity0=sentiment/positive/eng
    Entity1=sentiment/negative/eng
    EntityField0=POSITIVE_VIBE
    EntityField1=NEGATIVE_VIBE
    EntityComponentField0=TOPIC,SENTIMENT
    EntityComponentField1=TOPIC,SENTIMENT

    // some invalid matches are given very low scores so that we can filter them out:
    MinScore=0.1

    // for extraction of Twitter handles, hashtags and emoticons:
    TangibleCharacters=@#:;

    // for displaying metadata:
    OutputScores=True
    OutputSimpleMatchInfo=False
    EnableComponents=True

For more information on the sentiment analysis grammar files, how to adjust the sentiment analysis by extending the grammars, and the features that the sentiment grammars support, refer to IDOL Expert.

## Perform Sentiment Analysis on Short Comments

The standard sentiment analysis grammars are designed for high precision. For some sources of short comment data, such as YouTube comments, no positive or negative matches are found in some documents despite sentiment clearly being expressed.

If recall with the full `sentiment_eng.ecr` grammar file is too low, and your documents are generally short comments, use `sentiment_basic_eng.ecr` to extract additional matches.This grammar contains carefully-selected lists of positive and negative terms that help determine the sentiment of a document in which `sentiment_eng.ecr` found no matches.

`sentiment_basic_eng.ecr` contains terms in title case, but research shows that for most data these impair recall, so these are given a lower score. HPE recommends that you set `EntityMinScoreN` to `0.4` to filter out these terms unless you need them.

`sentiment_basic_eng.ecr` does not expose TOPIC or SENTIMENT components, and does not use scores to reflect strength or reliability of polarity. The following additional example configuration shows the recommended usage:

    [Eduction]
    ResourceFiles=grammars/sentiment_eng.ecr,grammars/sentiment_basic_eng.ecr
    // optional further layer of analysis for very short documents:
    Entity2=sentiment/basic_positive/eng
    Entity3=sentiment/basic_negative/eng
    EntityField2=BASIC_POSITIVE_VIBE
    EntityField3=BASIC_NEGATIVE_VIBE
    // remove this setting to include basic matches in titlecase - this is not recommended because on most data it decreases precision:
    EntityMinScore2=0.4
    EntityMinScore3=0.4

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