The Boolean Query filter supports the logical operators, grouping syntax, and other Boolean elements listed below. For substantive examples, see Boolean Query Examples.
Logical Operators
| Notation | Description | Example |
|---|---|---|
term1 AND _term2_ | Returns sound bites that contain both term1 and term2. | This example matches mentions containing both “coke” AND “love”: coke AND love |
term1 OR _term2_ | Returns sound bites that contain either term1 or term2. | This example matches mentions containing either “coke” OR “love”: coke OR love |
AND NOT _term_ | Omits sound bites that contain term. | The following example matches mentions of “Walmart” that do not also contain “coupon”: Walmart AND NOT coupon |
Grouping Syntax
| Notation | Description | Example |
|---|---|---|
(_Boolean_ expression_) | Specifies the order for processing expressions. You can add up to five levels of nested parentheses. | This example directs Quid Monitor to process the beginning OR expression first, then the ending OR expression, and finally the AND expression: ( sentence:"General Motors" OR sentence:GM ) AND ( sentence:car OR sentence:automobile OR sentence:vehicle OR sentence:auto ) |
""_term_"" | Omits stemmed variants of term from the results. See Keyword Matching for detailed information. | The following example matches content containing the term “play,” but not any of its stemmed variants, such as “played,” “playing,” or “plays”: ""play"" |
"_phrase_" | Matches content containing the specified phrase. NOTE: Using double quotation marks does not suppress stemming. See Keyword Matching for detailed information. | The following example matches occurrences of “google” and “play” in the same sentence: sentence:google play This example only matches occurrences of “google play” in the same sentence: sentence:"google play" |
Wildcards
| Notation | Description | Example |
|---|---|---|
| * | Matches multiple variants in the same way as structured mode (see Using Wildcards in Filters for more information): - primary: supports preceding, embedded, and trailing wildcards, including those containing hashtags and @mentions.- sentence:, title:, paragraph:, and document: support preceding or embedded wildcards.- author: supports embedded and trailing wildcards.- hashtag: supports embedded and trailing wildcards.- mentionedAuthor: supports embedded and trailing wildcards.NOTES: - The sentence:, title:, paragraph:, and document: options do not support the wildcard formats #_term_* or #_term_*. You can, however, use these formats with the hashtag: filter.- You cannot use a wildcard in a term surrounded by quotation marks ( "_term_") or double double quotation marks (""_term_"").- You can only specify wildcards in topics that use the Decahose feed. | sentence:coke* AND mentionedAuthor:@*coke* AND hashtag:*coke* AND author:*dell* AND domain:twitter.com |
Primary Terms
| Notation | Topics | Themes | Description | Example |
|---|---|---|---|---|
primary:_term_ | Yes | No | Does the following: - Identifies term as an object to which sentiment refers (to support high precision sentiment)- Hides _ term_by default in word clouds- Displays matches for term on the Tuner tab- Downloads Instagram posts for _ term_if it is a hashtag | The following example specifies “coke,” “#coke,” and “@cocacola” as primary terms: primary:coke OR primary:#coke OR primary:@cocacola |
Scope Options
| Notation | Description | Example |
|---|---|---|
term~_number_of_words_ | Matches mentions containing the specified terms within the specified number of words from each other. You can use the ~ operator with these scope options: t itle: sentence: paragraph: document: peers: NOTES: - The maximum value for number_of_words is 30.- You cannot use the ~ operator with exact match, only with phrase match.- The ~ operator only works for terms occurring within a sentence. For example, the expression to the right would not match this sentence: “Coke = diabetes. Boycott!” | This example matches mentions where a sentence or its previous or subsequent sentences contain “coke” and “boycott” within three words: paragraph:"coke boycott"~3 |
sentence:_term_ | (Default scope option) Returns sentences containing the specified term. | This example returns sentences containing “boycott” and “coke” in the same sentence: sentence:boycott AND sentence:coke |
paragraph:_term_ | Returns paragraphs (the hit sentence +/1 sentence) containing the specified term. | The following example returns paragraphs containing “blue” and “black” in the same paragraph (+/- 1 sentence): paragraph:blue AND paragraph:black |
peers_N_ | Returns N sentences that occur before and after the sentence matching the query. N can be a value between 1 and 10 (where peers1 is equivalent to the paragraph option). | The following example returns all mentions of Tide that also have a mention of “cleaning” within +/-4 sentences: Tide AND peers4:cleaning |
document:_term_ | Returns documents containing the specified term. | This example returns documents containing “orange” and “white” when they appear in the same document or in a document’s title document:orange AND document:white |
Filters
Quid Monitor provides a wide range of Boolean filters. Note that many of the advanced filters are available in themes only.
| Syntax | Supported In | Description | Standard Mode Equivalent? |
|---|---|---|---|
author:_username_ | Topics Themes | Matches content posted by the specified author handle or account name. See Filtering by Author in Boolean for details on using this filter. IMPORTANT: This filter does not return Facebook or YouTube data. Example: author:ladygaga | Yes |
authorName:_display_name_ | Topics Themes | Matches content posted by the specified author display name or handle. See Filtering by Author in Boolean for details on using this filter. Example: authorName:ladygaga | No |
bio:_term_ | Themes only | Matches content posted by authors with the specified term in their bio. Example bio:mom | Yes |
conversationCategory:Ad | AutoPost | JobPost | NewsUrl | Porn | Themes only | Matches content classified as the specified type to filter out unwanted data from your results, such as ads or pornography. NOTE: This filter returns only Twitter data. Example: conversationCategory:Ad OR conversationCategory:Porn | No |
domain:_domain_name_ | Topics Themes | Matches content derived from the specified domain or subdomain. See Entering Domains in Filters for domain syntax and formatting guidelines. Example: domain:twitter.com | Yes, but Boolean supports additional domain types. |
emphasis:true | false | Themes only | When set to true, retrieves only sound bites where NetBase has detected strong sentiment. This can occur when a sentiment word itself is strong, such as “love,” or when other words intensify the sentiment, such as “really like” or “tastes so gooooooooooooooood.” Example: emphasis:true NOTES: - The sentiment in the sound bite might or might not refer directly to primary term(s), which means that not all results have high precision sentiment; however, you can filter on positives or negatives to view only sound bites with high precision sentiment. - NetBase supports this feature for all NLP languages. | No |
gender:"male" | "female" | "unknown" | Audience Topics Themes | Match content from authors with the specified gender only. Example: gender:"female" | Yes |
geo:"_locale_" | Topics Themes | Matches content originating from the specified locale. See Boolean Geography Values for a complete list of supported values. Example: geo:"Switzerland" | Yes |
geoFenceCircle geoFencePolygon geoFenceRectangle NOTE: Do not specify these tags manually. Use the Geo-Fence Selector in the | Themes only | Matches content published within the circumscribed area. Example: geoFenceCircle: "radius=581707.9791495572|coordinates=[(63.547465,-137.314454)]" | Yes, but you can only add one geo-fence per topic. In themes, you can add multiple geo-fences up to the Boolean query character and term limits. |
feature:HasAnaphora | Themes only | Matches content containing an anaphoric reference to an antecedent. For example, “I find great gear all the time. Try Nike they make great running gear… ” where Nike is the antecedent. | No |
feature:HasCurrencySign | Themes only | Matches content containing a currency symbol, such as $, £, or €. | No |
feature:HasDate | Themes only | Matches content containing a reference to a date or time, such as “Sunday,” “now,” or “this year.” | No |
feature:HasExclamation | Themes only | Matches content containing an exclamation mark (!). | No |
feature:HasNegEmo | Themes only | Matches content containing a negative emoji or emoticon. For example: “Um… Thank you Life Lesson Geico spoiler commercial…” AVeryMerryToystore | No |
feature:HasPosEmo | Themes only | Matches content containing a positive emoji or emoticon. For example: “Also, switching to Geico could save you 15% or more in car insurance!!!! “ | No |
feature:HasQuestionMark | Themes only | Matches content containing a question mark (?). | No |
feature:HasQuotation | Themes only | Matches content containing a single or double quotation mark (’ or ”). | No |
feature:HasURL | Themes only | Matches content containing a URL. | No |
forumName:_forum_ | Topics Themes | Matches content from the specified forum. This option supports a wildcard as a suffix or within the value. This filter allows you to retrieve data across all forum domains that are potentially relevant to your brand or from sub-sections of a specific forum domain, as in the following examples: NOTE: This option matches only posts classified with the forum source type. It does not return posts from other source types, such as news, even if the specified forumName value appears in the document’s title. | No |
hashtag:_hashtag_ | Topics Themes | Matches content containing the specified hashtag in both post text and post metadata. You do not have to specify the pound sign. This example matches Twitter and Instagram content containing the hashtag Oscars: hashtag:oscars | No standard filter; however, NetBase searches metadata for hashtags by default in standard mode |
interest:"interest" | Audience Topics Themes | Matches content from authors with one or more of the predefined interests in their bio text. Example: interest:"Arts and Crafts"For a list of supported interest values, see Interests and Professions Coverage. | Yes |
language:"_language_" | Topics Themes | Matches content authored in the specified language. See Boolean Language Values for a complete list of supported values. Exceptions: - Boolean does not support a value that indicates all languages. - To match sound bites without a language classification, enter Unknown. Example: language:"Japanese" | Yes |
LexisNexis® News | Topics Themes | See Boolean Query Filters for LexisNexis News for a full list of supported Boolean query filters specific to LexisNexis News. | Yes |
logoHigh:"_logo_name_" logoMedium: "_logo_name_" logoLow: "_logo_name_" | Themes only | Matches content containing the specified logo (you can only specify logos that have been added your account). The high, medium, and low filter variants control the level of noise in your results. Less precise options return more false positives. Because the volume of false positives varies from logo to logo, NetBase recommends starting with logoLow:, then experiment to see how many false positives are eliminated by using logoHigh:.NOTE: The logoLow: filter is particularly useful for finding brand or logo infringements. Example: logoHigh:"Jim Beam" | Yes |
mediaType:photo | video | audio | link | nomedia | Themes only | Matches sound bites with images or image links, videos or video links, audio files or audio links, or links with a type other than the above. Specify nomedia to match sound bites with no attachments or links. This option returns text-only sound bites. Example: mediaType:photo | Yes |
mentionedAuthor:author_name | Topics Themes | Matches content containing the specified author_name. Example: mentionedAuthor:SteveJobs OR BillGates | No |
metaTag:"_meta_tag_" | Topics Themes | (Supported for VoC data only) Matches content by filtering on meta tags using one of the following formats: - metaTag:Product Line searches for sound bites with meta tags containing the specified value, either as a standalone meta tag, a meta tag type, or a meta tag value.- metaTag:Product Line=Dolls searches for sound bites containing the specified meta tag type set to the specified meta tag value.- metaTag:"Product Line=Action Figures" is the same as the above syntax except that it also returns meta tag specifications containing spaces.Example: metaTag:"First Visit"NOTES: - This filter does not work on user-added tags (assigned from the Edit tab or Stream widget) in VoC topics. To filter on user-added tags, use the powerTag filter.- All metaTag values are case-insensitive.- NetBase matches against each part of the meta_tag_type=_meta___tag_type_ syntax separately. For example, you can match the meta tag Sides=Chips using any of the following values: metaTag:Sides, metaTag:Chips, or metaTag:Sides=Chips | Yes |
minFollower:"value"maxFollower:"value" | Audience topics Themes | Matches content from authors with the specified minimum or maximum number of followers. Example: minFollower:"5K" AND maxFollower:"1M"NOTES: - The value specified in this filter must match one of the predefined values on the Followers/Daily Visitors filter slider. Enter a value of 0, 1, or 5 to view the Boolean auto-suggested integers that match the slider. - If left blank, the minFollower default value is 0.- If left blank, the maxFollower default value is the highest available follower count (100M). | Yes |
netPromoterScores | Themes only | (Supported for VoC data only) Matches content by filtering on specified overall net promoter scores (-100 to 100). NOTE: You cannot specify a value range like you can in the non-Boolean Net Promoter Scores filter (such as “5-7”)—you must list out each individual value. Example: netPromoterScores:10 OR netPromoterScores:9 | Yes |
overallStarRatings | Themes only | (Supported for VoC data only) Matches content by filtering on specified overall star rating scores (1-5). Example: overallStarRatings:4 OR overallStarRatings:5 | Yes |
overallSurveyScores | Themes only | (Supported for VoC data only) Matches content by filtering on specified overall survey scores (1-10). Example: overallSurveyScores:10 OR overallSurveyScores:9 | Yes |
personalNarratives:true | false | Themes only | Set the personalNarratives option to true to retrieve only sound bites in which a first person singular pronoun (such as English “I”, “my,” or Spanish “yo”) or verb (such as Spanish “amo”) occurs. This option helps to filter out noise and surfaces sound bites that were written from a personal perspective, such as “@swimgirl3836 love my Dior red pants.” Example: personalNarratives:true NOTES: - The personalNarratives filter applies independently of whether or not the sentence contains any sentiment.- This filter does not retrieve sentences containing plural pronouns or verbs, such as “we,” “our,” or “vamos.” - Quid Monitor supports this filter for all NLP Plus and NLP languages. | No |
postId:_post_id_ | Themes only | Matches only posts with the specified ID. Example: postId:BRDRDT-559298224.7xbcfi.du7xix5 In the unlikely event that two posts have the same ID on different domains, you can AND the domain name as shown below: Example: postId:BRDRDT-559298224.7xbcfi.du7xix5 AND domain:reddit.com | Yes |
postType:"Original" | "Reposts" | "Replies and Comments" | Audience topics Themes | Match content from authors of one or more of the following post types: - Original - Reposts - Replies and Comments Example: postType:"Reposts"See Posts Types Filter for additional information. | Yes |
powerTag:_tag_ | Themes only | Matches user-tagged content (assigned on the Edit tab or in the Stream widget) by filtering on tags using one of the following formats: - powerTag:Escalate searches for sound bites with tags containing the specified value, either as a standalone tag, a tag type, or a tag value.- powerTag:Dept=PR searches for sound bites containing the specified tag type set to the specified tag value.- powerTag:"Dept = PR" is the same as the above syntax except that it also returns tag specifications containing spaces.Example: powerTag:Emotion=Sarcastic NOTES: - All powerTag values are case-insensitive. - NetBase matches against each part of the tag_type=tag_type syntax separately. For example, you can match the tag DEPT=PR using any of the following values: powerTag:DEPT, powerTag:PR, or powerTag:DEPT=PR. | Yes |
productHierarchy:_hierarchy_level_ | Topics Themes | (Supported for VoC data only) Matches content by filtering on product hierarchy levels using one of the following formats: - productHierarchy:Recommended searches for sound bites with product hierarchy levels containing the specified value, either as a standalone product hierarchy level, a product hierarchy level type, or a product hierarchy level value.- productHierarchy:Recommended=Yes searches for sound bites containing the specified product hierarchy level type set to the specified product hierarchy levelvalue.- "productHierarchy:Sides="Chips and Salsa" is the same as the above syntax except that it also returns product hierarchy levels containing spaces.NOTES: - You can add up to ten productHierarchy levels.- All productHierarchy values are case-insensitive.- Quid Monitor matches against each part of the hierarchy_level_name=hierarchy_level_value syntax separately.For example, you can match the product hierarchy Recommended=Yes using any of the following values: productHierarchy:Recommended, productHierarchy:Yesor productHierarchy:Recommended=Yes. | Yes |
profession:"profession" | Audience Topics Themes | Matches content from authors with one or more of the predefined professions in their bio text. Example: profession:"Education"For a list of supported profession values, see Interests and Professions Coverage. | Yes |
sourceType:Blogs | Comments | ConsumerReviews | Facebook | Forums | Instagram | Microblogs | News | Other | ProfReviews | SocialNetworks | TikTok | Twitter | YouTube | Topics Themes | Matches content from the specified source type. Example: sourceType:Blogs | Yes |
starRatings:_type_=_value_ | Themes only | (Supported for VoC data only) Matches content by filtering on the specified star rating type set to the specified value. Example: starRatings:Cost=5 NOTE: The type values you can specify depend on how your organization maps internal data fields. | Yes |
surveyScores:_type_=_value_ | Themes only | (Supported for VoC data only) Matches content by filtering on the specified survey score type set to the specified value. Example: surveyScores:Performance=10 NOTE: The type values you can specify depend on how your organization maps internal data fields. | Yes |
timezone:_time_zone_ | Themes only | Filters data by time zone. This allows you to expand the coverage of a geography-specific topic to focus on a language OR a geographic area OR a time zone. See Boolean Time Zone Values for a complete list of supported values. If the time zone value contains spaces, enclose the value in quotation marks. Example: timezone:"Europe/Paris" NOTE: Many time zones have the same UTC offset. Be careful to specify the correct time zone for the area you want to focus on. | No |
title:_term_ | Topics Themes | Matches content in documents whose title contains the specified term. Example: title:Mac AND NOT title:Jobs |
NOTES
- You can specify multiple values for one filter, such as
mentionedAuthors:SteveJobs OR BillGates. This is the equivalent ofmentionedAuthors:SteveJobs OR mentionedAuthors:BillGates.- Quid Monitor enables you to combine a Boolean expression with some standard filters. This is useful for:
- Applying filters that are not supported in Boolean, such as Include and Exclude Geographies.
- Quickly adding items after you have defined and saved a Boolean query. For example, if define a complex query that uses many
domain:filters and you later discover additional domains that you want to exclude, you can simply add them to the Exclude Domains filter instead of having to edit the Boolean expression