dashboard: >
    Twitter Mining - Suport Vector Machine and Term Frequency–Inverse Document Frequency

    ====================================================================================


    The process is divided into three steps to the tweets mining process:

    (h) Type “h” and hit the Enter key to see the more help.


    (c) 1ª step: Collect tweets from Twitter

    (t) 2ª step: Training some tweets

    (p) 3ª step: Predication others tweets

    (x) for exit

training: >

    How would you rate this tweet?


    (1) Positive, (2) Negative, (3) Neutral or (4) Unknown

help: >

    Available commands


    tweets metrics
      ~> you see metrics of all tweets.

    tweets
      ~> you see all tweets collected.

    tweets trained
      ~> you see all tweets trained

    tweets trained positive
      ~> you see all tweets trained with evaluation positive

    tweets trained negative
      ~> you see all tweets trained with evaluation negative

    tweets trained neutral
      ~> you see all tweets trained with evaluation neutral

    tweets trained unknown
      ~> you see all tweets trained with evaluation unknown

tweet: >
    @{username}: {tweet}

    Date: {date}

tweet_trained: >
    @{username}: {tweet}

    Evaluation: {evaluation}

metrics: >

    Quantity of tweets: {total}

    Quantity of tweets trained: {trained}

    Quantity of tweets available to predict: {predict}

error:
    arg_not_found: >

        Argument for search tweets no found!


        For collect tweets with word python, type: c python

success:
    collected: "Successfully collected Tweets"

dashboard: >
  Twitter Mining - Suport Vector Machine and Term Frequency–Inverse Document Frequency

  ====================================================================================


  The process is divided into three steps to the tweets mining process:

  (h) Type “h” and hit the Enter key to see the more help.


  (c) 1ª step: Collect tweets from Twitter

  (t) 2ª step: Training some tweets

  (p) 3ª step: Predication others tweets

  (x) for exit

training: >

  How would you rate this tweet?


  (1) Positive, (2) Negative, (3) Neutral or (4) Unknown

help: >

  Available commands


  tweets metrics
    ~> you see metrics of all tweets.

  tweets
    ~> you see all tweets collected.

  tweets trained
    ~> you see all tweets trained

  tweets trained positive
    ~> you see all tweets trained with evaluation positive

  tweets trained negative
    ~> you see all tweets trained with evaluation negative

  tweets trained neutral
    ~> you see all tweets trained with evaluation neutral

  tweets trained unknown
    ~> you see all tweets trained with evaluation unknown

tweet: >
  @{username}: {tweet}

  Date: {date}

tweet_trained: >
  @{username}: {tweet}

  Evaluation: {evaluation}

metrics: >

  Quantity of tweets: {total}

  Quantity of tweets trained: {trained}

  Quantity of tweets available to predict: {predict}

error:
  arg_not_found: >
    Argument for search tweets no found!


    Example for collect tweets with word python, type: c python

success:
  collected: "Successfully collected Tweets"

Fix tab space of yaml
Fix tab space of yaml
